Script to validate points in ReSurvey database using RS data (Satellite Embeddings Dataset)
Validation done with ALL points (all observations)
Alicia Valdés
23 septiembre 2025
This R script is used to validate the points in the ReSurvey database using only bands from the Google Satellite Embedding dataset: https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_SATELLITE_EMBEDDING_V1_ANNUAL#description
Load libraries
library(tidyverse)
library(here)
library(gridExtra)
library(readxl)
library(scales)
library(sf)
library(rnaturalearth)
library(dtplyr)
library(lme4)
library(lmerTest)
library(car)
library(ggeffects)
library(party)
library(partykit)
library(moreparty)
library(doParallel)
library(strucchange)
library(ggparty)
library(caret)
library(moreparty)
library(randomForest)
library(pROC)
library(corrplot)
library(rlang)
library(stringr)
library(beepr)
library(foreach)
library(permimp)
library(yardstick)Define printall function
printall <- function(tibble) {
print(tibble, width = Inf)
}Load geom_flat_violin plot
source("https://gist.githubusercontent.com/benmarwick/2a1bb0133ff568cbe28d/raw/fb53bd97121f7f9ce947837ef1a4c65a73bffb3f/geom_flat_violin.R")Load previously created objects
# Define the folder path
folder_path <- here("objects", "RF", "Satellite_Embeddings")
# List all .RData or .rda files in the folder
rdata_files <- list.files(folder_path, full.names = TRUE)
# Load each file
lapply(rdata_files, load, envir = .GlobalEnv)list()Read data
data_validation <- read_tsv(here(
"data", "clean","final_RS_data_SatEmb_20250922.csv"))No parsing issues!
Some data managenemt
TO-DO: Missing data checks
Do when all RS data is ready!
Distributions all bioregions
# Define a function to create histograms
plot_histogram <- function(data, x_var, x_label) {
ggplot(data %>%
dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")),
aes(x = !!sym(x_var))) +
geom_histogram(color = "black", fill = "white") +
labs(x = x_label, y = "Frequency") +
theme_bw()
}# Define a function to create plots with violin + boxplot + points
distr_plot <- function(data, y_vars, y_labels) {
for (i in seq_along(y_vars)) {
y_var <- y_vars[[i]]
y_label <- y_labels[[i]]
p <- ggplot(data = data %>%
dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")),
aes(x = EUNISa_1_descr, y = !!sym(y_var), fill = EUNISa_1_descr)) +
geom_flat_violin(position = position_nudge(x = 0.2, y = 0), alpha = 0.8) +
geom_point(aes(y = !!sym(y_var), color = EUNISa_1_descr),
position = position_jitter(width = 0.15), size = 1, alpha = 0.25) +
geom_boxplot(width = 0.2, outlier.shape = NA, alpha = 0.5) +
stat_summary(fun.y = mean, geom = "point", shape = 20, size = 1) +
stat_summary(fun.data = function(x) data.frame(y = max(x) + 0.1,
label = length(x)),
geom = "text", aes(label = ..label..), vjust = 0.5) +
labs(y = y_label, x = "EUNIS level 1") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 15)) +
guides(fill = FALSE, color = FALSE) +
theme_bw() + coord_flip()
print(p)
}
}Histograms to check that max and min values are ok:
plot_histogram(data_validation, "A00", "A00")
plot_histogram(data_validation, "A01", "A01")
plot_histogram(data_validation, "A23", "A23")
plot_histogram(data_validation, "A32", "A32")
plot_histogram(data_validation, "A50", "A50")
plot_histogram(data_validation, "A61", "A61")
plot_histogram(data_validation, "A63", "A63")
Distribution plots:
distr_plot(data_validation,
c("A00", "A01", "A10", "A22", "A33",
"A40", "A53", "A61", "A62", "A63"),
c("A00", "A01", "A10", "A22", "A33",
"A40", "A53", "A61", "A62", "A63"))
Distributions per bioregion
# Define a function to create plots with violin + boxplot + points
distr_plot_biogeo <- function(data, y_vars, y_labels) {
plots <- list()
for (i in seq_along(y_vars)) {
y_var <- y_vars[[i]]
y_label <- y_labels[[i]]
p <- ggplot(data = data %>%
dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")),
aes(x = EUNISa_1_descr, y = !!sym(y_var), fill = EUNISa_1_descr)) +
geom_flat_violin(position = position_nudge(x = 0.2, y = 0), alpha = 0.8) +
geom_point(aes(y = !!sym(y_var), color = EUNISa_1_descr),
position = position_jitter(width = 0.15), size = 1, alpha = 0.25) +
geom_boxplot(width = 0.2, outlier.shape = NA, alpha = 0.5) +
stat_summary(fun.y = mean, geom = "point", shape = 20, size = 1) +
stat_summary(fun.data = function(x) data.frame(y = max(x) + 0.1,
label = length(x)),
geom = "text", aes(label = ..label..), vjust = 0.5) +
labs(y = y_label, x = "EUNISa_1_descr") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 15)) +
guides(fill = FALSE, color = FALSE) +
theme_bw() + coord_flip() + facet_wrap(~ biogeo)
plots[[y_var]] <- p
}
return(plots)
}Distribution plots:
distr_plot_biogeo(data_validation,
c("A00", "A01", "A10", "A22", "A33",
"A40", "A53", "A61", "A62", "A63"),
c("A00", "A01", "A10", "A22", "A33",
"A40", "A53", "A61", "A62", "A63"))$A00
$A01
$A10
$A22
$A33
$A40
$A53
$A61
$A62
$A63
Define functions for RF models
Function for fitting RF models
RF models fitted using the conditional inference version of random forest (first cforest in package party, now fastcforest in package moreparty). Suggested if the data are highly correlated. Cforest is more stable in deriving variable importance values in the presence of highly correlated variables, thus providing better accuracy in calculating variable importance (ref below).
Hothorn, T., Hornik, K. and Zeileis, A. (2006) Unbiased Recursive Portioning: A Conditional Inference Framework. Journal of Computational and Graphical Statistics, 15, 651- 674. http://dx.doi.org/10.1198/106186006X133933
Choose the hyperparameter mtry based on the square root of the number of predictor variables:
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. Springer Science & Business Media.
Maybe TO_DO: We variated ntree from 50 to 800 in steps of 50, leaving mtry constant at 2. Tis parameter variation showed that ntree=500 was optimal, while higher ntree led to no further model improvement (Supplementary Fig. S10). Subsequently, the hyperparameter mtry was varied from 2 to 8 with constant ntree=500. Here, mtry=3 led to the best results in almost all cases (Supplementary Fig. S11). Consequently, we chose ntree=500 and mtry=3 for our main analysis across all study sites.
Define a function to run fastcforest models:
run_rf <- function(vars_RF, train_data, response_var, ntree = 500)
{
# Detect and register available cores (leave one free)
n_cores <- parallel::detectCores() - 1
cl <- makeCluster(n_cores)
registerDoParallel(cl)
train_name <- deparse(substitute(train_data))
# Export necessary variables to the cluster
clusterExport(cl, varlist = c("vars_RF", "train_data", "response_var"),
envir = environment())
# Set seed for reproducibility
set.seed(123)
# Measure execution time
execution_time <- system.time({
rf_model <- fastcforest(
formula = reformulate(vars_RF, response = response_var),
data = train_data,
controls = party::cforest_control(
mtry = round(sqrt(length(vars_RF))),
ntree = ntree
),
parallel = TRUE
)
})
# Stop the cluster
stopCluster(cl)
# Return both the model and execution time
list(model = rf_model, time = execution_time)
}Function to compute variable importance
# compute_varimp <- function(model, nperm = 100,
# n_cores = parallel::detectCores() - 1) {
# # Set up parallel backend
# cl <- makeCluster(n_cores)
# registerDoParallel(cl)
#
# # Measure execution time
# execution_time <- system.time({
# varimp_list <- foreach(i = 1:nperm, .combine = '+',
# .packages = "party") %dopar% {
# varimp(model, conditional = FALSE, nperm = 1)
# }
# })
#
# stopCluster(cl)
#
# # Average the results
# varimp_avg <- varimp_list / nperm
#
# return(list(varimp = varimp_avg, time = execution_time))
# }Using permimp() en permimp package: https://cran.r-project.org/web/packages/permimp/vignettes/permimp-package.html#fn1
compute_varimp <- function(model, nperm = 100) {
# Measure execution time
execution_time <- system.time({
varimp_result <- permimp(model, conditional = FALSE, progressBar = TRUE)
})
return(list(varimp = varimp_result, time = execution_time))
}Function to compute CONDITIONAL variable importance
compute_varimp_cond <- function(model, nperm = 100) {
# Measure execution time
execution_time <- system.time({
varimp_result <- permimp(model, conditional = TRUE, progressBar = TRUE)
})
return(list(varimp = varimp_result, time = execution_time))
}Function to compute ROC (level 1)
compute_roc_level1 <- function(model, test_data) {
# Measure execution time
execution_time <- system.time({
# Step 1: Predict probabilities
probabilities <- predict(model, newdata = test_data, type = "prob")
# Step 2: Convert list of matrices to a proper data frame
prob_matrix <- t(sapply(probabilities, as.vector))
prob_df <- as.data.frame(prob_matrix)
colnames(prob_df) <- c("Q", "R", "S", "T")
# Step 3: Prepare actual class labels
actual <- factor(test_data$EUNISa_1, levels = c("Q", "R", "S", "T"))
# Step 4: Binarize actual labels
actual_bin <- model.matrix(~ actual - 1)
colnames(actual_bin) <- gsub("actual", "", colnames(actual_bin))
# Step 5: Compute ROC data for each class
roc_data <- lapply(levels(actual), function(class) {
roc_obj <- roc(actual_bin[, class], prob_df[[class]])
auc_val <- round(auc(roc_obj), 3)
data.frame(
FPR = rev(roc_obj$specificities),
TPR = rev(roc_obj$sensitivities),
Class = paste0(class, " (AUC = ", auc_val, ")")
)
}) %>% bind_rows()
})
# Return both ROC data and execution time
return(list(roc = roc_data, time = execution_time))
}Function to compute ROC (level 2)
compute_roc_level2 <- function(model, test_data) {
# Measure execution time
execution_time <- system.time({
# Step 1: Predict probabilities
probabilities <- predict(model, newdata = test_data, type = "prob")
# Step 2: Convert list of matrices to a proper data frame
prob_matrix <- t(sapply(probabilities, as.vector))
prob_df <- as.data.frame(prob_matrix)
colnames(prob_df) <- c("Q1", "Q2", "Q4", "Q5", "R1", "R2", "R3", "R4", "R5",
"R6", "S3", "S4", "T1", "T3")
# Step 3: Prepare actual class labels
actual <- factor(test_data$EUNISa_2,
levels = c("Q1", "Q2", "Q4", "Q5", "R1", "R2", "R3", "R4",
"R5", "R6", "S3", "S4", "T1", "T3"))
# Step 4: Binarize actual labels
actual_bin <- model.matrix(~ actual - 1)
colnames(actual_bin) <- gsub("actual", "", colnames(actual_bin))
# Step 5: Compute ROC data for each class
roc_data <- lapply(levels(actual), function(class) {
roc_obj <- roc(actual_bin[, class], prob_df[[class]])
auc_val <- round(auc(roc_obj), 3)
data.frame(
FPR = rev(roc_obj$specificities),
TPR = rev(roc_obj$sensitivities),
Class = paste0(class, " (AUC = ", auc_val, ")")
)
}) %>% bind_rows()
})
# Return both ROC data and execution time
return(list(roc = roc_data, time = execution_time))
}Define list of predictor vars
vars_RF_SatEmb <- colnames(
data_validation)[startsWith(colnames(data_validation), "A")]
vars_RF_SatEmb_CH <- c(vars_RF_SatEmb, "canopy_height")Correlation
Correlation of all variables to be included in RF models:
corrplot(data_validation %>%
dplyr::select(starts_with("A")) %>%
cor(use = "pairwise.complete.obs"),
method = "color", type = "upper", tl.col = "black", tl.srt = 45)
corrplot(data_validation %>%
dplyr::select(starts_with("A"), canopy_height) %>%
cor(use = "pairwise.complete.obs"),
method = "color", type = "upper", tl.col = "black", tl.srt = 45)
Rough validation
Define a set of rules for a first validation of ALL ReSurvey data (“Expert-based” rules). Not very ambitious, only taking out observations that are clearly wrong on the basis of indicator values.
Create column for first validation based on different indicators, where “wrong” is noted when the validation rule is not met.
Here only using validation based on canopy height, not NDWI.
Define rules:
data_validation <-
data_validation %>%
mutate(
valid_1_CH = case_when(
# R & Q points with high CH
EUNISa_1 %in% c("R", "Q") & canopy_height > 2 ~ "wrong",
# T points with low CH
EUNISa_1 == "T" & canopy_height < 3 ~ "wrong",
# S points with high CH
EUNISa_1 == "S" & canopy_height > 3 ~ "wrong",
TRUE ~ NA_character_),
# Count how many validation rules are not met
valid_1_count = rowSums(across(c(valid_1_CH), ~ . == "wrong"),
na.rm = TRUE),
# Points where at least 1 rule not met
valid_1 = if_else(valid_1_count > 0, "At least 1 rule broken",
"No rules broken so far")
)Fit RF models (level 1)
Without refinement
Get filtered data
# No validation
data_validation_novalid <- data_validation %>%
# Select only GPS points
dplyr::filter(Lctnmth == "Location with GPS" |
Lctnmth == "Location with differential GPS") %>%
select(-Lctnmth) %>%
mutate(EUNISa_1 = as.factor(EUNISa_1))
# Rough validation
data_validation_roughvalid <- data_validation %>%
# Select only GPS points
dplyr::filter(Lctnmth == "Location with GPS" |
Lctnmth == "Location with differential GPS") %>%
# Filter out points that have not passed the rough validation
dplyr::filter(valid_1 == "No rules broken so far") %>%
select(-Lctnmth, -valid_1) %>%
mutate(EUNISa_1 = as.factor(EUNISa_1))N points per category
bind_rows(
data_validation_novalid %>% count(EUNISa_1) %>%
pivot_wider(names_from = EUNISa_1, values_from = n) %>%
mutate(data = "data_validation_novalid") %>%
select(data, Q, R, S, T),
data_validation_roughvalid %>% count(EUNISa_1) %>%
pivot_wider(names_from = EUNISa_1, values_from = n) %>%
mutate(data = "data_validation_roughvalid") %>%
select(data, Q, R, S, T),
)Split into training and test data sets
set.seed(123)train_indices_novalid <- sample(1:nrow(data_validation_novalid),
0.7 * nrow(data_validation_novalid))
train_indices_roughvalid <- sample(1:nrow(data_validation_roughvalid),
0.7 * nrow(data_validation_roughvalid))train_data_novalid <- data_validation_novalid[train_indices_novalid, ]
train_data_roughvalid <- data_validation_roughvalid[train_indices_roughvalid, ]test_data_novalid <- data_validation_novalid[-train_indices_novalid, ]
test_data_roughvalid <- data_validation_roughvalid[-train_indices_roughvalid, ]Fit models
print(rf1$time) user system elapsed
9.89 4.89 110.48 print(rf2$time) user system elapsed
6.44 3.24 102.18 print(rf3$time) user system elapsed
4.73 2.67 86.47 print(rf4$time) user system elapsed
6.47 2.42 103.50 Predictions
Confusion matrices
# 1: No validation, SatEmb bands
confusionMatrix(predictions_rf1, test_data_novalid$EUNISa_1)Confusion Matrix and Statistics
Reference
Prediction Q R S T
Q 919 126 71 3
R 155 1753 114 48
S 48 70 508 0
T 0 1 6 69
Overall Statistics
Accuracy : 0.835
95% CI : (0.823, 0.8465)
No Information Rate : 0.5012
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.7343
Mcnemar's Test P-Value : 1.558e-13
Statistics by Class:
Class: Q Class: R Class: S Class: T
Sensitivity 0.8191 0.8990 0.7268 0.57500
Specificity 0.9278 0.8367 0.9630 0.99814
Pos Pred Value 0.8213 0.8469 0.8115 0.90789
Neg Pred Value 0.9268 0.8918 0.9415 0.98663
Prevalence 0.2884 0.5012 0.1796 0.03084
Detection Rate 0.2362 0.4505 0.1306 0.01773
Detection Prevalence 0.2876 0.5320 0.1609 0.01953
Balanced Accuracy 0.8734 0.8678 0.8449 0.78657# 2: No validation, SatEmb bands + CH
confusionMatrix(predictions_rf2, test_data_novalid$EUNISa_1)Confusion Matrix and Statistics
Reference
Prediction Q R S T
Q 918 124 74 5
R 160 1744 112 47
S 44 79 510 3
T 0 3 3 65
Overall Statistics
Accuracy : 0.8319
95% CI : (0.8198, 0.8435)
No Information Rate : 0.5012
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.7295
Mcnemar's Test P-Value : 2.116e-11
Statistics by Class:
Class: Q Class: R Class: S Class: T
Sensitivity 0.8182 0.8944 0.7296 0.54167
Specificity 0.9267 0.8357 0.9605 0.99841
Pos Pred Value 0.8189 0.8454 0.8019 0.91549
Neg Pred Value 0.9264 0.8873 0.9419 0.98560
Prevalence 0.2884 0.5012 0.1796 0.03084
Detection Rate 0.2359 0.4482 0.1311 0.01671
Detection Prevalence 0.2881 0.5302 0.1635 0.01825
Balanced Accuracy 0.8724 0.8650 0.8451 0.77004# 3: Rough validation, SatEmb bands
confusionMatrix(predictions_rf3, test_data_roughvalid$EUNISa_1)Confusion Matrix and Statistics
Reference
Prediction Q R S T
Q 865 133 76 3
R 116 1660 125 11
S 59 58 419 0
T 0 2 4 55
Overall Statistics
Accuracy : 0.8363
95% CI : (0.8238, 0.8483)
No Information Rate : 0.5167
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.7321
Mcnemar's Test P-Value : 2.815e-07
Statistics by Class:
Class: Q Class: R Class: S Class: T
Sensitivity 0.8317 0.8958 0.6715 0.79710
Specificity 0.9167 0.8546 0.9605 0.99829
Pos Pred Value 0.8032 0.8682 0.7817 0.90164
Neg Pred Value 0.9303 0.8847 0.9328 0.99603
Prevalence 0.2900 0.5167 0.1740 0.01924
Detection Rate 0.2412 0.4629 0.1168 0.01534
Detection Prevalence 0.3003 0.5332 0.1495 0.01701
Balanced Accuracy 0.8742 0.8752 0.8160 0.89770# 4: Rough validation, SatEmb bands + CH
confusionMatrix(predictions_rf4, test_data_roughvalid$EUNISa_1)Confusion Matrix and Statistics
Reference
Prediction Q R S T
Q 854 131 69 1
R 131 1670 116 1
S 55 52 439 0
T 0 0 0 67
Overall Statistics
Accuracy : 0.845
95% CI : (0.8327, 0.8567)
No Information Rate : 0.5167
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.7464
Mcnemar's Test P-Value : NA
Statistics by Class:
Class: Q Class: R Class: S Class: T
Sensitivity 0.8212 0.9012 0.7035 0.97101
Specificity 0.9211 0.8569 0.9639 1.00000
Pos Pred Value 0.8095 0.8707 0.8040 1.00000
Neg Pred Value 0.9265 0.8903 0.9391 0.99943
Prevalence 0.2900 0.5167 0.1740 0.01924
Detection Rate 0.2381 0.4657 0.1224 0.01868
Detection Prevalence 0.2942 0.5349 0.1523 0.01868
Balanced Accuracy 0.8711 0.8791 0.8337 0.98551Variable importance
Unconditional
Plots
plot(varimp_rf1$varimp, margin = c(10, 6, 2, 2))
plot(varimp_rf2$varimp, margin = c(10, 6, 2, 2))
plot(varimp_rf3$varimp, margin = c(10, 6, 2, 2))
plot(varimp_rf4$varimp, margin = c(10, 6, 2, 2))HERE
No validation
No refinement
SatEmb bands
SatEmb + CH
Refinement 10-90th: 1 variable
Refinement 10-90th: 2 variables
Refinement 10-90th: 3 variables
Refinement 10-90th: 4 variables
Rough validation
Without refinement
Get filtered data
data_validation_GPS <- data_validation %>%
# Select only GPS points
dplyr::filter(Lctnmth == "Location with GPS" |
Lctnmth == "Location with differential GPS") %>%
select(-Lctnmth) %>%
mutate(EUNISa_1 = as.factor(EUNISa_1))N points per category
data_validation_GPS %>% count(EUNISa_1)Split into training and test data sets
set.seed(123)train_indices1 <- sample(1:nrow(data_validation_GPS),
0.7 * nrow(data_validation_GPS))train_data1 <- data_validation_GPS[train_indices1, ]test_data1 <- data_validation_GPS[-train_indices1, ]Fit models
print(rf1$time) user system elapsed
5.53 3.15 75.04 print(rf1_CH$time) user system elapsed
15.82 3.89 79.82 Predictions
Confusion matrix
confusionMatrix(predictions_rf1, test_data1$EUNISa_1)Confusion Matrix and Statistics
Reference
Prediction Q R S T
Q 909 123 61 3
R 155 1756 118 54
S 58 70 515 1
T 0 1 5 62
Overall Statistics
Accuracy : 0.8332
95% CI : (0.8211, 0.8448)
No Information Rate : 0.5012
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.7311
Mcnemar's Test P-Value : 1.112e-13
Statistics by Class:
Class: Q Class: R Class: S Class: T
Sensitivity 0.8102 0.9005 0.7368 0.51667
Specificity 0.9325 0.8315 0.9596 0.99841
Pos Pred Value 0.8294 0.8430 0.7997 0.91176
Neg Pred Value 0.9238 0.8927 0.9433 0.98483
Prevalence 0.2884 0.5012 0.1796 0.03084
Detection Rate 0.2336 0.4513 0.1324 0.01593
Detection Prevalence 0.2817 0.5353 0.1655 0.01748
Balanced Accuracy 0.8713 0.8660 0.8482 0.75754confusionMatrix(predictions_rf1_CH, test_data1$EUNISa_1)Confusion Matrix and Statistics
Reference
Prediction Q R S T
Q 898 121 76 6
R 172 1756 114 52
S 52 70 504 0
T 0 3 5 62
Overall Statistics
Accuracy : 0.8276
95% CI : (0.8153, 0.8393)
No Information Rate : 0.5012
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.7215
Mcnemar's Test P-Value : 7.107e-15
Statistics by Class:
Class: Q Class: R Class: S Class: T
Sensitivity 0.8004 0.9005 0.7210 0.51667
Specificity 0.9267 0.8259 0.9618 0.99788
Pos Pred Value 0.8156 0.8386 0.8051 0.88571
Neg Pred Value 0.9197 0.8920 0.9403 0.98482
Prevalence 0.2884 0.5012 0.1796 0.03084
Detection Rate 0.2308 0.4513 0.1295 0.01593
Detection Prevalence 0.2830 0.5382 0.1609 0.01799
Balanced Accuracy 0.8635 0.8632 0.8414 0.75727Variable importance
Unconditional
Plot
plot(varimp_rf1$varimp, margin = c(10, 6, 2, 2))Error en h(simpleError(msg, call)):
error al evaluar el argumento 'x' al seleccionar un método para la función 'plot': objeto 'varimp_rf1' no encontradoConditional (TBD)
Plot
<!-- rnb-source-begin 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 -->
```r
plot(varimp-cond_rf1$varimp, margin = c(9, 3, 2, 2))
plot(varimp-cond_rf2$varimp, margin = c(9, 3, 2, 2))
plot(varimp-cond_rf3$varimp, margin = c(9, 3, 2, 2))
plot(varimp-cond_rf4$varimp, margin = c(9, 3, 2, 2))
# plot(varimp-cond_rf5$varimp, margin = c(9, 3, 2, 2))
# plot(varimp-cond_rf6$varimp, margin = c(9, 3, 2, 2))
# plot(varimp-cond_rf7$varimp, margin = c(9, 3, 2, 2))
# plot(varimp-cond_rf8$varimp, margin = c(9, 3, 2, 2))
plot(varimp-cond_rf9$varimp, margin = c(9, 3, 2, 2))
plot(varimp-cond_rf10$varimp, margin = c(9, 3, 2, 2))
plot(varimp-cond_rf11$varimp, margin = c(9, 3, 2, 2))
plot(varimp-cond_rf12$varimp, margin = c(9, 3, 2, 2))
# plot(varimp-cond_rf13$varimp, margin = c(9, 3, 2, 2))
# plot(varimp-cond_rf14$varimp, margin = c(9, 3, 2, 2))
# plot(varimp-cond_rf15$varimp, margin = c(9, 3, 2, 2))
# plot(varimp-cond_rf16$varimp, margin = c(9, 3, 2, 2))
plot(varimp-cond_rf17$varimp, margin = c(9, 3, 2, 2))
plot(varimp-cond_rf18$varimp, margin = c(9, 3, 2, 2))
plot(varimp-cond_rf19$varimp, margin = c(9, 3, 2, 2))
plot(varimp-cond_rf20$varimp, margin = c(9, 3, 2, 2))
# plot(varimp-cond_rf21$varimp, margin = c(9, 3, 2, 2))
# plot(varimp-cond_rf22$varimp, margin = c(9, 3, 2, 2))
# plot(varimp-cond_rf23$varimp, margin = c(9, 3, 2, 2))
# plot(varimp-cond_rf24$varimp, margin = c(9, 3, 2, 2))
```
<!-- rnb-source-end -->```r
plot(varimp-cond_rf1$varimp, margin = c(9, 3, 2, 2))
plot(varimp-cond_rf2$varimp, margin = c(9, 3, 2, 2))
plot(varimp-cond_rf3$varimp, margin = c(9, 3, 2, 2))
plot(varimp-cond_rf4$varimp, margin = c(9, 3, 2, 2))
# plot(varimp-cond_rf5$varimp, margin = c(9, 3, 2, 2))
# plot(varimp-cond_rf6$varimp, margin = c(9, 3, 2, 2))
# plot(varimp-cond_rf7$varimp, margin = c(9, 3, 2, 2))
# plot(varimp-cond_rf8$varimp, margin = c(9, 3, 2, 2))
plot(varimp-cond_rf9$varimp, margin = c(9, 3, 2, 2))
plot(varimp-cond_rf10$varimp, margin = c(9, 3, 2, 2))
plot(varimp-cond_rf11$varimp, margin = c(9, 3, 2, 2))
plot(varimp-cond_rf12$varimp, margin = c(9, 3, 2, 2))
# plot(varimp-cond_rf13$varimp, margin = c(9, 3, 2, 2))
# plot(varimp-cond_rf14$varimp, margin = c(9, 3, 2, 2))
# plot(varimp-cond_rf15$varimp, margin = c(9, 3, 2, 2))
# plot(varimp-cond_rf16$varimp, margin = c(9, 3, 2, 2))
plot(varimp-cond_rf17$varimp, margin = c(9, 3, 2, 2))
plot(varimp-cond_rf18$varimp, margin = c(9, 3, 2, 2))
plot(varimp-cond_rf19$varimp, margin = c(9, 3, 2, 2))
plot(varimp-cond_rf20$varimp, margin = c(9, 3, 2, 2))
# plot(varimp-cond_rf21$varimp, margin = c(9, 3, 2, 2))
# plot(varimp-cond_rf22$varimp, margin = c(9, 3, 2, 2))
# plot(varimp-cond_rf23$varimp, margin = c(9, 3, 2, 2))
# plot(varimp-cond_rf24$varimp, margin = c(9, 3, 2, 2))
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### ROC curves (TBD)
<!-- rnb-text-end -->
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<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
print(roc_data1$time)
print(roc_data2$time)
print(roc_data3$time)
print(roc_data4$time)
# print(roc_data5$time)
# print(roc_data6$time)
# print(roc_data7$time)
# print(roc_data8$time)
print(roc_data9$time)
print(roc_data10$time)
print(roc_data11$time)
print(roc_data12$time)
# print(roc_data13$time)
# print(roc_data14$time)
# print(roc_data15$time)
# print(roc_data16$time)
print(roc_data17$time)
print(roc_data18$time)
print(roc_data19$time)
print(roc_data20$time)
# print(roc_data21$time)
# print(roc_data22$time)
# print(roc_data23$time)
# print(roc_data24$time)
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
<!-- rnb-source-begin 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 -->
```r
```r
print(roc_data1$time)
print(roc_data2$time)
print(roc_data3$time)
print(roc_data4$time)
# print(roc_data5$time)
# print(roc_data6$time)
# print(roc_data7$time)
# print(roc_data8$time)
print(roc_data9$time)
print(roc_data10$time)
print(roc_data11$time)
print(roc_data12$time)
# print(roc_data13$time)
# print(roc_data14$time)
# print(roc_data15$time)
# print(roc_data16$time)
print(roc_data17$time)
print(roc_data18$time)
print(roc_data19$time)
print(roc_data20$time)
# print(roc_data21$time)
# print(roc_data22$time)
# print(roc_data23$time)
# print(roc_data24$time)
```
```
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<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\nsave(roc_data1, \n     file = \\objects/RF/S2/rf1_rocdata_l1_slope_novalid_GPS_S2.Rdata\\)\nsave(roc_data2,\n     file = \\objects/RF/S2/rf2_rocdata_l1_slope_novalid_GPSdiff_S2.Rdata\\)\nsave(roc_data3,\n     file = \\objects/RF/S2/rf3_rocdata_l1_slope_roughvalid_GPS_S2.Rdata\\)\nsave(roc_data4,\n     file = \\objects/RF/S2/rf4_rocdata_l1_slope_roughvalid_GPSdiff_S2.Rdata\\)\n# save(roc_data5, \n#      file = \\objects/RF/S2/rf5_rocdata_l1_slope_roughvalid-refin1090_GPS_S2.Rdata\\)\n# save(roc_data6, \n#      file = \\objects/RF/S2/rf6_rocdata_l1_slope_roughvalid-refin1090_GPSdiff_S2.Rdata\\)\n# save(roc_data7,\n#      file = \\objects/RF/S2/rf7_rocdata_l1_slope_roughvalid-refin2080_GPS_S2.Rdata\\)\n# save(roc_data8, \n#      file = \\objects/RF/S2/rf8_rocdata_l1_slope_roughvalid-refin2080_GPSdiff_S2.Rdata\\)\nsave(roc_data9,\n     file = \\objects/RF/S2/rf9_rocdata_l1_threshold_novalid_GPS_S2.Rdata\\)\nsave(roc_data10,\n     file = \\objects/RF/S2/rf10_rocdata_l1_threshold_novalid_GPSdiff_S2.Rdata\\)\nsave(roc_data11, \n     file = \\objects/RF/S2/rf11_rocdata_l1_threshold_roughvalid_GPS_S2.Rdata\\)\nsave(roc_data12, \n     file = \\objects/RF/S2/rf12_rocdata_l1_threshold_roughvalid_GPSdiff_S2.Rdata\\)\n# save(roc_data13, \n#      file = \\objects/RF/S2/rf13_rocdata_l1_threshold_roughvalid-refin1090_GPS_S2.Rdata\\)\n# save(roc_data14, \n#      file = \\objects/RF/S2/rf14_rocdata_l1_threshold_roughvalid-refin1090_GPSdiff_S2.Rdata\\)\n# save(roc_data15,\n#      file = \\objects/RF/S2/rf15_rocdata_l1_threshold_roughvalid-refin2080_GPS_S2.Rdata\\)\n# save(roc_data16, \n#      file = \\objects/RF/S2/rf16_rocdata_l1_threshold_roughvalid-refin2080_GPSdiff_S2.Rdata\\)\nsave(roc_data17, \n     file = \\objects/RF/S2/rf17_rocdata_l1_months_novalid_GPS_S2.Rdata\\)\nsave(roc_data18, \n     file = \\objects/RF/S2/rf18_rocdata_l1_months_novalid_GPSdiff_S2.Rdata\\)\nsave(roc_data19, \n     file = \\objects/RF/S2/rf19_rocdata_l1_months_roughvalid_GPS_S2.Rdata\\)\nsave(roc_data20, \n     file = \\objects/RF/S2/rf20_rocdata_l1_months_roughvalid_GPSdiff_S2.Rdata\\)\n# save(roc_data21,\n#      file = \\objects/RF/S2/rf21_rocdata_l1_months_roughvalid-refin1090_GPS_S2.Rdata\\)\n# save(roc_data22, \n#      file = \\objects/RF/S2/rf22_rocdata_l1_months_roughvalid-refin1090_GPSdiff_S2.Rdata\\)\n# save(roc_data23, \n#      file = \\objects/RF/S2/rf23_rocdata_l1_months_roughvalid-refin2080_GPS_S2.Rdata\\)\n# save(roc_data24, \n#      file = \\objects/RF/S2/rf24_rocdata_l1_months_roughvalid-refin2080_GPSdiff_S2.Rdata\\)\n```\n\n<!-- rnb-source-end -->\n"} -->
save(roc_data1,
file = \objects/RF/S2/rf1_rocdata_l1_slope_novalid_GPS_S2.Rdata\)
save(roc_data2,
file = \objects/RF/S2/rf2_rocdata_l1_slope_novalid_GPSdiff_S2.Rdata\)
save(roc_data3,
file = \objects/RF/S2/rf3_rocdata_l1_slope_roughvalid_GPS_S2.Rdata\)
save(roc_data4,
file = \objects/RF/S2/rf4_rocdata_l1_slope_roughvalid_GPSdiff_S2.Rdata\)
# save(roc_data5,
# file = \objects/RF/S2/rf5_rocdata_l1_slope_roughvalid-refin1090_GPS_S2.Rdata\)
# save(roc_data6,
# file = \objects/RF/S2/rf6_rocdata_l1_slope_roughvalid-refin1090_GPSdiff_S2.Rdata\)
# save(roc_data7,
# file = \objects/RF/S2/rf7_rocdata_l1_slope_roughvalid-refin2080_GPS_S2.Rdata\)
# save(roc_data8,
# file = \objects/RF/S2/rf8_rocdata_l1_slope_roughvalid-refin2080_GPSdiff_S2.Rdata\)
save(roc_data9,
file = \objects/RF/S2/rf9_rocdata_l1_threshold_novalid_GPS_S2.Rdata\)
save(roc_data10,
file = \objects/RF/S2/rf10_rocdata_l1_threshold_novalid_GPSdiff_S2.Rdata\)
save(roc_data11,
file = \objects/RF/S2/rf11_rocdata_l1_threshold_roughvalid_GPS_S2.Rdata\)
save(roc_data12,
file = \objects/RF/S2/rf12_rocdata_l1_threshold_roughvalid_GPSdiff_S2.Rdata\)
# save(roc_data13,
# file = \objects/RF/S2/rf13_rocdata_l1_threshold_roughvalid-refin1090_GPS_S2.Rdata\)
# save(roc_data14,
# file = \objects/RF/S2/rf14_rocdata_l1_threshold_roughvalid-refin1090_GPSdiff_S2.Rdata\)
# save(roc_data15,
# file = \objects/RF/S2/rf15_rocdata_l1_threshold_roughvalid-refin2080_GPS_S2.Rdata\)
# save(roc_data16,
# file = \objects/RF/S2/rf16_rocdata_l1_threshold_roughvalid-refin2080_GPSdiff_S2.Rdata\)
save(roc_data17,
file = \objects/RF/S2/rf17_rocdata_l1_months_novalid_GPS_S2.Rdata\)
save(roc_data18,
file = \objects/RF/S2/rf18_rocdata_l1_months_novalid_GPSdiff_S2.Rdata\)
save(roc_data19,
file = \objects/RF/S2/rf19_rocdata_l1_months_roughvalid_GPS_S2.Rdata\)
save(roc_data20,
file = \objects/RF/S2/rf20_rocdata_l1_months_roughvalid_GPSdiff_S2.Rdata\)
# save(roc_data21,
# file = \objects/RF/S2/rf21_rocdata_l1_months_roughvalid-refin1090_GPS_S2.Rdata\)
# save(roc_data22,
# file = \objects/RF/S2/rf22_rocdata_l1_months_roughvalid-refin1090_GPSdiff_S2.Rdata\)
# save(roc_data23,
# file = \objects/RF/S2/rf23_rocdata_l1_months_roughvalid-refin2080_GPS_S2.Rdata\)
# save(roc_data24,
# file = \objects/RF/S2/rf24_rocdata_l1_months_roughvalid-refin2080_GPSdiff_S2.Rdata\)
<!-- rnb-output-end -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\n```r\nsave(roc_data1, \n     file = \\objects/RF/S2/rf1_rocdata_l1_slope_novalid_GPS_S2.Rdata\\)\nsave(roc_data2,\n     file = \\objects/RF/S2/rf2_rocdata_l1_slope_novalid_GPSdiff_S2.Rdata\\)\nsave(roc_data3,\n     file = \\objects/RF/S2/rf3_rocdata_l1_slope_roughvalid_GPS_S2.Rdata\\)\nsave(roc_data4,\n     file = \\objects/RF/S2/rf4_rocdata_l1_slope_roughvalid_GPSdiff_S2.Rdata\\)\n# save(roc_data5, \n#      file = \\objects/RF/S2/rf5_rocdata_l1_slope_roughvalid-refin1090_GPS_S2.Rdata\\)\n# save(roc_data6, \n#      file = \\objects/RF/S2/rf6_rocdata_l1_slope_roughvalid-refin1090_GPSdiff_S2.Rdata\\)\n# save(roc_data7,\n#      file = \\objects/RF/S2/rf7_rocdata_l1_slope_roughvalid-refin2080_GPS_S2.Rdata\\)\n# save(roc_data8, \n#      file = \\objects/RF/S2/rf8_rocdata_l1_slope_roughvalid-refin2080_GPSdiff_S2.Rdata\\)\nsave(roc_data9,\n     file = \\objects/RF/S2/rf9_rocdata_l1_threshold_novalid_GPS_S2.Rdata\\)\nsave(roc_data10,\n     file = \\objects/RF/S2/rf10_rocdata_l1_threshold_novalid_GPSdiff_S2.Rdata\\)\nsave(roc_data11, \n     file = \\objects/RF/S2/rf11_rocdata_l1_threshold_roughvalid_GPS_S2.Rdata\\)\nsave(roc_data12, \n     file = \\objects/RF/S2/rf12_rocdata_l1_threshold_roughvalid_GPSdiff_S2.Rdata\\)\n# save(roc_data13, \n#      file = \\objects/RF/S2/rf13_rocdata_l1_threshold_roughvalid-refin1090_GPS_S2.Rdata\\)\n# save(roc_data14, \n#      file = \\objects/RF/S2/rf14_rocdata_l1_threshold_roughvalid-refin1090_GPSdiff_S2.Rdata\\)\n# save(roc_data15,\n#      file = \\objects/RF/S2/rf15_rocdata_l1_threshold_roughvalid-refin2080_GPS_S2.Rdata\\)\n# save(roc_data16, \n#      file = \\objects/RF/S2/rf16_rocdata_l1_threshold_roughvalid-refin2080_GPSdiff_S2.Rdata\\)\nsave(roc_data17, \n     file = \\objects/RF/S2/rf17_rocdata_l1_months_novalid_GPS_S2.Rdata\\)\nsave(roc_data18, \n     file = \\objects/RF/S2/rf18_rocdata_l1_months_novalid_GPSdiff_S2.Rdata\\)\nsave(roc_data19, \n     file = \\objects/RF/S2/rf19_rocdata_l1_months_roughvalid_GPS_S2.Rdata\\)\nsave(roc_data20, \n     file = \\objects/RF/S2/rf20_rocdata_l1_months_roughvalid_GPSdiff_S2.Rdata\\)\n# save(roc_data21,\n#      file = \\objects/RF/S2/rf21_rocdata_l1_months_roughvalid-refin1090_GPS_S2.Rdata\\)\n# save(roc_data22, \n#      file = \\objects/RF/S2/rf22_rocdata_l1_months_roughvalid-refin1090_GPSdiff_S2.Rdata\\)\n# save(roc_data23, \n#      file = \\objects/RF/S2/rf23_rocdata_l1_months_roughvalid-refin2080_GPS_S2.Rdata\\)\n# save(roc_data24, \n#      file = \\objects/RF/S2/rf24_rocdata_l1_months_roughvalid-refin2080_GPSdiff_S2.Rdata\\)\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin 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 -->
```r
```r
save(roc_data1,
file = \objects/RF/S2/rf1_rocdata_l1_slope_novalid_GPS_S2.Rdata\)
save(roc_data2,
file = \objects/RF/S2/rf2_rocdata_l1_slope_novalid_GPSdiff_S2.Rdata\)
save(roc_data3,
file = \objects/RF/S2/rf3_rocdata_l1_slope_roughvalid_GPS_S2.Rdata\)
save(roc_data4,
file = \objects/RF/S2/rf4_rocdata_l1_slope_roughvalid_GPSdiff_S2.Rdata\)
# save(roc_data5,
# file = \objects/RF/S2/rf5_rocdata_l1_slope_roughvalid-refin1090_GPS_S2.Rdata\)
# save(roc_data6,
# file = \objects/RF/S2/rf6_rocdata_l1_slope_roughvalid-refin1090_GPSdiff_S2.Rdata\)
# save(roc_data7,
# file = \objects/RF/S2/rf7_rocdata_l1_slope_roughvalid-refin2080_GPS_S2.Rdata\)
# save(roc_data8,
# file = \objects/RF/S2/rf8_rocdata_l1_slope_roughvalid-refin2080_GPSdiff_S2.Rdata\)
save(roc_data9,
file = \objects/RF/S2/rf9_rocdata_l1_threshold_novalid_GPS_S2.Rdata\)
save(roc_data10,
file = \objects/RF/S2/rf10_rocdata_l1_threshold_novalid_GPSdiff_S2.Rdata\)
save(roc_data11,
file = \objects/RF/S2/rf11_rocdata_l1_threshold_roughvalid_GPS_S2.Rdata\)
save(roc_data12,
file = \objects/RF/S2/rf12_rocdata_l1_threshold_roughvalid_GPSdiff_S2.Rdata\)
# save(roc_data13,
# file = \objects/RF/S2/rf13_rocdata_l1_threshold_roughvalid-refin1090_GPS_S2.Rdata\)
# save(roc_data14,
# file = \objects/RF/S2/rf14_rocdata_l1_threshold_roughvalid-refin1090_GPSdiff_S2.Rdata\)
# save(roc_data15,
# file = \objects/RF/S2/rf15_rocdata_l1_threshold_roughvalid-refin2080_GPS_S2.Rdata\)
# save(roc_data16,
# file = \objects/RF/S2/rf16_rocdata_l1_threshold_roughvalid-refin2080_GPSdiff_S2.Rdata\)
save(roc_data17,
file = \objects/RF/S2/rf17_rocdata_l1_months_novalid_GPS_S2.Rdata\)
save(roc_data18,
file = \objects/RF/S2/rf18_rocdata_l1_months_novalid_GPSdiff_S2.Rdata\)
save(roc_data19,
file = \objects/RF/S2/rf19_rocdata_l1_months_roughvalid_GPS_S2.Rdata\)
save(roc_data20,
file = \objects/RF/S2/rf20_rocdata_l1_months_roughvalid_GPSdiff_S2.Rdata\)
# save(roc_data21,
# file = \objects/RF/S2/rf21_rocdata_l1_months_roughvalid-refin1090_GPS_S2.Rdata\)
# save(roc_data22,
# file = \objects/RF/S2/rf22_rocdata_l1_months_roughvalid-refin1090_GPSdiff_S2.Rdata\)
# save(roc_data23,
# file = \objects/RF/S2/rf23_rocdata_l1_months_roughvalid-refin2080_GPS_S2.Rdata\)
# save(roc_data24,
# file = \objects/RF/S2/rf24_rocdata_l1_months_roughvalid-refin2080_GPSdiff_S2.Rdata\)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### Plot
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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 -->\n\n```r\nroc1 <- ggplot(roc_data1$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\nroc2 <- ggplot(roc_data2$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\nroc3 <- ggplot(roc_data3$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\nroc4 <- ggplot(roc_data4$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\n# roc5 <- ggplot(roc_data5$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n# roc6 <- ggplot(roc_data6$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n# roc7 <- ggplot(roc_data7$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n# roc8 <- ggplot(roc_data8$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\nroc9 <- ggplot(roc_data9$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\nroc10 <- ggplot(roc_data10$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\nroc11 <- ggplot(roc_data11$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\nroc12 <- ggplot(roc_data12$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\n# roc13 <- ggplot(roc_data13$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n# roc14 <- ggplot(roc_data14$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n# roc15 <- ggplot(roc_data15$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n# roc16 <- ggplot(roc_data16$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\nroc17 <- ggplot(roc_data17$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\nroc18 <- ggplot(roc_data18$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\nroc19 <- ggplot(roc_data19$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\nroc20 <- ggplot(roc_data20$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\n# roc21 <- ggplot(roc_data21$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n# roc22 <- ggplot(roc_data22$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n# roc23 <- ggplot(roc_data23$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n# roc24 <- ggplot(roc_data24$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n```\n\n<!-- rnb-source-end -->\n"} -->
roc1 <- ggplot(roc_data1$roc, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
y = \True Positive Rate\, color = \Class (AUC)\) +
theme_minimal() + theme(legend.position = \bottom\)
roc2 <- ggplot(roc_data2$roc, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
y = \True Positive Rate\, color = \Class (AUC)\) +
theme_minimal() + theme(legend.position = \bottom\)
roc3 <- ggplot(roc_data3$roc, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
y = \True Positive Rate\, color = \Class (AUC)\) +
theme_minimal() + theme(legend.position = \bottom\)
roc4 <- ggplot(roc_data4$roc, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
y = \True Positive Rate\, color = \Class (AUC)\) +
theme_minimal() + theme(legend.position = \bottom\)
# roc5 <- ggplot(roc_data5$roc, aes(x = FPR, y = TPR, color = Class)) +
# geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
# labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
# y = \True Positive Rate\, color = \Class (AUC)\) +
# theme_minimal() + theme(legend.position = \bottom\)
# roc6 <- ggplot(roc_data6$roc, aes(x = FPR, y = TPR, color = Class)) +
# geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
# labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
# y = \True Positive Rate\, color = \Class (AUC)\) +
# theme_minimal() + theme(legend.position = \bottom\)
# roc7 <- ggplot(roc_data7$roc, aes(x = FPR, y = TPR, color = Class)) +
# geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
# labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
# y = \True Positive Rate\, color = \Class (AUC)\) +
# theme_minimal() + theme(legend.position = \bottom\)
# roc8 <- ggplot(roc_data8$roc, aes(x = FPR, y = TPR, color = Class)) +
# geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
# labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
# y = \True Positive Rate\, color = \Class (AUC)\) +
# theme_minimal() + theme(legend.position = \bottom\)
roc9 <- ggplot(roc_data9$roc, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
y = \True Positive Rate\, color = \Class (AUC)\) +
theme_minimal() + theme(legend.position = \bottom\)
roc10 <- ggplot(roc_data10$roc, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
y = \True Positive Rate\, color = \Class (AUC)\) +
theme_minimal() + theme(legend.position = \bottom\)
roc11 <- ggplot(roc_data11$roc, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
y = \True Positive Rate\, color = \Class (AUC)\) +
theme_minimal() + theme(legend.position = \bottom\)
roc12 <- ggplot(roc_data12$roc, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
y = \True Positive Rate\, color = \Class (AUC)\) +
theme_minimal() + theme(legend.position = \bottom\)
# roc13 <- ggplot(roc_data13$roc, aes(x = FPR, y = TPR, color = Class)) +
# geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
# labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
# y = \True Positive Rate\, color = \Class (AUC)\) +
# theme_minimal() + theme(legend.position = \bottom\)
# roc14 <- ggplot(roc_data14$roc, aes(x = FPR, y = TPR, color = Class)) +
# geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
# labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
# y = \True Positive Rate\, color = \Class (AUC)\) +
# theme_minimal() + theme(legend.position = \bottom\)
# roc15 <- ggplot(roc_data15$roc, aes(x = FPR, y = TPR, color = Class)) +
# geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
# labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
# y = \True Positive Rate\, color = \Class (AUC)\) +
# theme_minimal() + theme(legend.position = \bottom\)
# roc16 <- ggplot(roc_data16$roc, aes(x = FPR, y = TPR, color = Class)) +
# geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
# labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
# y = \True Positive Rate\, color = \Class (AUC)\) +
# theme_minimal() + theme(legend.position = \bottom\)
roc17 <- ggplot(roc_data17$roc, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
y = \True Positive Rate\, color = \Class (AUC)\) +
theme_minimal() + theme(legend.position = \bottom\)
roc18 <- ggplot(roc_data18$roc, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
y = \True Positive Rate\, color = \Class (AUC)\) +
theme_minimal() + theme(legend.position = \bottom\)
roc19 <- ggplot(roc_data19$roc, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
y = \True Positive Rate\, color = \Class (AUC)\) +
theme_minimal() + theme(legend.position = \bottom\)
roc20 <- ggplot(roc_data20$roc, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
y = \True Positive Rate\, color = \Class (AUC)\) +
theme_minimal() + theme(legend.position = \bottom\)
# roc21 <- ggplot(roc_data21$roc, aes(x = FPR, y = TPR, color = Class)) +
# geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
# labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
# y = \True Positive Rate\, color = \Class (AUC)\) +
# theme_minimal() + theme(legend.position = \bottom\)
# roc22 <- ggplot(roc_data22$roc, aes(x = FPR, y = TPR, color = Class)) +
# geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
# labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
# y = \True Positive Rate\, color = \Class (AUC)\) +
# theme_minimal() + theme(legend.position = \bottom\)
# roc23 <- ggplot(roc_data23$roc, aes(x = FPR, y = TPR, color = Class)) +
# geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
# labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
# y = \True Positive Rate\, color = \Class (AUC)\) +
# theme_minimal() + theme(legend.position = \bottom\)
# roc24 <- ggplot(roc_data24$roc, aes(x = FPR, y = TPR, color = Class)) +
# geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
# labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
# y = \True Positive Rate\, color = \Class (AUC)\) +
# theme_minimal() + theme(legend.position = \bottom\)
<!-- rnb-output-end -->
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 -->\n\n```r\n```r\nroc1 <- ggplot(roc_data1$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\nroc2 <- ggplot(roc_data2$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\nroc3 <- ggplot(roc_data3$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\nroc4 <- ggplot(roc_data4$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\n# roc5 <- ggplot(roc_data5$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n# roc6 <- ggplot(roc_data6$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n# roc7 <- ggplot(roc_data7$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n# roc8 <- ggplot(roc_data8$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\nroc9 <- ggplot(roc_data9$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\nroc10 <- ggplot(roc_data10$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\nroc11 <- ggplot(roc_data11$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\nroc12 <- ggplot(roc_data12$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\n# roc13 <- ggplot(roc_data13$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n# roc14 <- ggplot(roc_data14$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n# roc15 <- ggplot(roc_data15$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n# roc16 <- ggplot(roc_data16$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\nroc17 <- ggplot(roc_data17$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\nroc18 <- ggplot(roc_data18$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\nroc19 <- ggplot(roc_data19$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\nroc20 <- ggplot(roc_data20$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\n# roc21 <- ggplot(roc_data21$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n# roc22 <- ggplot(roc_data22$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n# roc23 <- ggplot(roc_data23$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n# roc24 <- ggplot(roc_data24$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n```\n```\n\n<!-- rnb-source-end -->\n"} -->
<!-- rnb-source-begin {"data":"```r\n```r\nroc1 <- ggplot(roc_data1$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\nroc2 <- ggplot(roc_data2$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\nroc3 <- ggplot(roc_data3$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\nroc4 <- ggplot(roc_data4$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\n# roc5 <- ggplot(roc_data5$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n# roc6 <- ggplot(roc_data6$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n# roc7 <- ggplot(roc_data7$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n# roc8 <- ggplot(roc_data8$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\nroc9 <- ggplot(roc_data9$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\nroc10 <- ggplot(roc_data10$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\nroc11 <- ggplot(roc_data11$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\nroc12 <- ggplot(roc_data12$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\n# roc13 <- ggplot(roc_data13$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n# roc14 <- ggplot(roc_data14$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n# roc15 <- ggplot(roc_data15$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n# roc16 <- ggplot(roc_data16$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\nroc17 <- ggplot(roc_data17$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\nroc18 <- ggplot(roc_data18$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\nroc19 <- ggplot(roc_data19$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\nroc20 <- ggplot(roc_data20$roc, aes(x = FPR, y = TPR, color = Class)) +\n  geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n  labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n       y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n  theme_minimal() + theme(legend.position = \\bottom\\)\n# roc21 <- ggplot(roc_data21$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n# roc22 <- ggplot(roc_data22$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n# roc23 <- ggplot(roc_data23$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n# roc24 <- ggplot(roc_data24$roc, aes(x = FPR, y = TPR, color = Class)) +\n#   geom_line(size = 1.2) + geom_abline(linetype = \\dashed\\, color = \\gray\\) +\n#   labs(title = \\Multiclass ROC Curves with AUC\\, x = \\False Positive Rate\\,\n#        y = \\True Positive Rate\\, color = \\Class (AUC)\\) +\n#   theme_minimal() + theme(legend.position = \\bottom\\)\n```\n```"} -->
```r
```r
roc1 <- ggplot(roc_data1$roc, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
y = \True Positive Rate\, color = \Class (AUC)\) +
theme_minimal() + theme(legend.position = \bottom\)
roc2 <- ggplot(roc_data2$roc, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
y = \True Positive Rate\, color = \Class (AUC)\) +
theme_minimal() + theme(legend.position = \bottom\)
roc3 <- ggplot(roc_data3$roc, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
y = \True Positive Rate\, color = \Class (AUC)\) +
theme_minimal() + theme(legend.position = \bottom\)
roc4 <- ggplot(roc_data4$roc, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
y = \True Positive Rate\, color = \Class (AUC)\) +
theme_minimal() + theme(legend.position = \bottom\)
# roc5 <- ggplot(roc_data5$roc, aes(x = FPR, y = TPR, color = Class)) +
# geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
# labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
# y = \True Positive Rate\, color = \Class (AUC)\) +
# theme_minimal() + theme(legend.position = \bottom\)
# roc6 <- ggplot(roc_data6$roc, aes(x = FPR, y = TPR, color = Class)) +
# geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
# labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
# y = \True Positive Rate\, color = \Class (AUC)\) +
# theme_minimal() + theme(legend.position = \bottom\)
# roc7 <- ggplot(roc_data7$roc, aes(x = FPR, y = TPR, color = Class)) +
# geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
# labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
# y = \True Positive Rate\, color = \Class (AUC)\) +
# theme_minimal() + theme(legend.position = \bottom\)
# roc8 <- ggplot(roc_data8$roc, aes(x = FPR, y = TPR, color = Class)) +
# geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
# labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
# y = \True Positive Rate\, color = \Class (AUC)\) +
# theme_minimal() + theme(legend.position = \bottom\)
roc9 <- ggplot(roc_data9$roc, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
y = \True Positive Rate\, color = \Class (AUC)\) +
theme_minimal() + theme(legend.position = \bottom\)
roc10 <- ggplot(roc_data10$roc, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
y = \True Positive Rate\, color = \Class (AUC)\) +
theme_minimal() + theme(legend.position = \bottom\)
roc11 <- ggplot(roc_data11$roc, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
y = \True Positive Rate\, color = \Class (AUC)\) +
theme_minimal() + theme(legend.position = \bottom\)
roc12 <- ggplot(roc_data12$roc, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
y = \True Positive Rate\, color = \Class (AUC)\) +
theme_minimal() + theme(legend.position = \bottom\)
# roc13 <- ggplot(roc_data13$roc, aes(x = FPR, y = TPR, color = Class)) +
# geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
# labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
# y = \True Positive Rate\, color = \Class (AUC)\) +
# theme_minimal() + theme(legend.position = \bottom\)
# roc14 <- ggplot(roc_data14$roc, aes(x = FPR, y = TPR, color = Class)) +
# geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
# labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
# y = \True Positive Rate\, color = \Class (AUC)\) +
# theme_minimal() + theme(legend.position = \bottom\)
# roc15 <- ggplot(roc_data15$roc, aes(x = FPR, y = TPR, color = Class)) +
# geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
# labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
# y = \True Positive Rate\, color = \Class (AUC)\) +
# theme_minimal() + theme(legend.position = \bottom\)
# roc16 <- ggplot(roc_data16$roc, aes(x = FPR, y = TPR, color = Class)) +
# geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
# labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
# y = \True Positive Rate\, color = \Class (AUC)\) +
# theme_minimal() + theme(legend.position = \bottom\)
roc17 <- ggplot(roc_data17$roc, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
y = \True Positive Rate\, color = \Class (AUC)\) +
theme_minimal() + theme(legend.position = \bottom\)
roc18 <- ggplot(roc_data18$roc, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
y = \True Positive Rate\, color = \Class (AUC)\) +
theme_minimal() + theme(legend.position = \bottom\)
roc19 <- ggplot(roc_data19$roc, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
y = \True Positive Rate\, color = \Class (AUC)\) +
theme_minimal() + theme(legend.position = \bottom\)
roc20 <- ggplot(roc_data20$roc, aes(x = FPR, y = TPR, color = Class)) +
geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
y = \True Positive Rate\, color = \Class (AUC)\) +
theme_minimal() + theme(legend.position = \bottom\)
# roc21 <- ggplot(roc_data21$roc, aes(x = FPR, y = TPR, color = Class)) +
# geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
# labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
# y = \True Positive Rate\, color = \Class (AUC)\) +
# theme_minimal() + theme(legend.position = \bottom\)
# roc22 <- ggplot(roc_data22$roc, aes(x = FPR, y = TPR, color = Class)) +
# geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
# labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
# y = \True Positive Rate\, color = \Class (AUC)\) +
# theme_minimal() + theme(legend.position = \bottom\)
# roc23 <- ggplot(roc_data23$roc, aes(x = FPR, y = TPR, color = Class)) +
# geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
# labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
# y = \True Positive Rate\, color = \Class (AUC)\) +
# theme_minimal() + theme(legend.position = \bottom\)
# roc24 <- ggplot(roc_data24$roc, aes(x = FPR, y = TPR, color = Class)) +
# geom_line(size = 1.2) + geom_abline(linetype = \dashed\, color = \gray\) +
# labs(title = \Multiclass ROC Curves with AUC\, x = \False Positive Rate\,
# y = \True Positive Rate\, color = \Class (AUC)\) +
# theme_minimal() + theme(legend.position = \bottom\)
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
roc1
roc2
roc3
roc4
# roc5
# roc6
# roc7
# roc8
roc9
roc10
roc11
roc12
# roc13
# roc14
# roc15
# roc16
roc17
roc18
roc19
roc20
# roc21
# roc22
# roc23
# roc24
<!-- rnb-output-end -->
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```r
```r
roc1
roc2
roc3
roc4
# roc5
# roc6
# roc7
# roc8
roc9
roc10
roc11
roc12
# roc13
# roc14
# roc15
# roc16
roc17
roc18
roc19
roc20
# roc21
# roc22
# roc23
# roc24
```
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
## With refinement
### Refinement
Based on the 1st, 2nd, 3rd, 4th... most important variables: A59, A15, A12, A60.
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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-->
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```r
distr_plot_percentiles <- function(data, y_vars, y_labels) {
for (i in seq_along(y_vars)) {
y_var <- y_vars[[i]]
y_label <- y_labels[[i]]
# Calculate percentiles per EUNISa_1 group
percentiles <- data %>%
group_by(EUNISa_1) %>%
summarise(
p10 = quantile(.data[[y_var]], 0.1, na.rm = TRUE),
p90 = quantile(.data[[y_var]], 0.9, na.rm = TRUE),
.groups = "drop"
)
# Join percentiles back to data
data_flagged <- data %>%
left_join(percentiles, by = "EUNISa_1") %>%
mutate(outlier_flag = case_when(
.data[[y_var]] < p10 ~ "low",
.data[[y_var]] > p90 ~ "high",
TRUE ~ "mid"
))
# Filter and plot
p <- ggplot(data = data_flagged %>%
dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")),
aes(x = EUNISa_1_descr, y = .data[[y_var]])) +
geom_flat_violin(aes(fill = EUNISa_1_descr),
position = position_nudge(x = 0.2, y = 0), alpha = 0.8) +
geom_point(aes(color = ifelse(outlier_flag == "mid",
EUNISa_1_descr, "grey")),
position = position_jitter(width = 0.15), size = 1,
alpha = 0.6) +
geom_boxplot(aes(fill = EUNISa_1_descr), width = 0.2, outlier.shape = NA,
alpha = 0.5) +
stat_summary(fun = mean, geom = "point", shape = 20, size = 1) +
stat_summary(fun.data = function(x) data.frame(y = max(x, na.rm = TRUE) +
0.1, label = length(x)),
geom = "text", aes(label = ..label..), vjust = 0.5) +
labs(y = y_label, x = "EUNIS level 1") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 15)) +
guides(fill = FALSE, color = FALSE) +
theme_bw() + coord_flip() +
scale_color_manual(values = c(
"Forests and other wooded land" = "#F8766D",
"Grasslands" = "#7CAE00",
"Heathlands, scrub and tundra" = "#00BFC4",
"Wetlands" = "#C77CFF",
"grey" = "grey"))
print(p)
}
}
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
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```r
distr_plot_percentiles(
# GPS points
data_validation_GPS,
c("A59", "A15", "A12", "A60"),
c("A59", "A15", "A12", "A60"))
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
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\" 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\" 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" />
<!-- rnb-plot-end -->
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qBYOOBVCaf5+BNluR02qLPB8OaKB2GDVVpuFevIiJUKc4w6MwNqY77C0o+otkHPYtF0nAFvSdgwmbz027+jU7yyCp08fiRrGzKTB3aeOpLmMry54sMkRW45oPn0hlWDUqNIPVnOY8D7YpxJU9NxMDUAAl6wvYeOCHi/+i/lfe/r6+Ry0dWLvkExugoZssTeAR1meHPFB3gtySDZuHvYyjbEMW+WPk1SxCxL6KUzxK9yxVpDDx3RD3+4cPGiiBYUvHTPHGQbAF5a5Ga+Evky9rxF4h28lhiX2Mw2pPdmyWrqW1imHOIpAvWQPXq8HjxoT265eJEesEdbMlGV0fLFCxcuwJP14AF79Aq/jBrr96msIpJ3rUEEGwTF/eaYt7r4De90NbzsVFfMYHjdanjZqa6YwfC61fCyU10xg+F1q+Flp7piBsPrVsPLTnXFDIbXrYaXneqKGQyvWw0vO9UVMxhetxpedqorZjC8bjW87FRXzGB43Wp42amumMHwutXwslNdMYPhdavhZae6YgbD61bDy051xQyG162Gl53qihkMr1sNLzvVFTMYXrcaXnaqK2YwvG41vOxUV8xgeN1qeNmprpjB8LrV8LJTXTGD4XWrUaZgP2xkSp3qytVgeN1q5CloXh8wBPHtlBntajC8bjWyFFLQJvltu1NduRoMr1uNjCuSx63Gt/VOdeVqMLxuNZIKZeg+8MAfrY0vw9ugMLx5CuXoArx1vS/D26AwvDkKVdhFeOuFvgxvg8LwZipUQlfCC/y216muXA2G161GrFCRXQ1v9diB4W1QGN60QlV0DXgr48vwNigMb0qhOrsmvBVjB4a3QWF4kwrV0U3AW8n5MrwNCsObUKjhd5PwVnG+DG+DwvBaCrXQTcNb7nwZ3gaF4bUU6rGbhrfU9zK8DQrDayjU9LtZ8Jb5Xoa3QWF4DYW67GbBW+J8Gd4GheHVUtvv5sBb6HwZ3gaF4VXSy0GxPrxFvpfhbVAYXim9BuEt8L0Mb4PC8JL0ClCsD2++82V4GxSGF6XXNLx5zpfhbVAY3kgP1RqFN8f5MrwNCsMbpxkahjfT+TK8DQrDG2fImoY3i16Gt0GZdXjNG9sbhzcDX4a3QZlleJOPZGgB3tQNbgxvgzKr8GY9S6QNeJP4MrwNykzCm/MQnHbgfcAKHhjeBmX24M1/fFNr8Br4MrwNyozBm09uu/BqfBneBmWG4C14ZN4U4J3ecyUZ3lriPbzpJ5Q6gBfxZXgblD0Mr/kk6DZQHEejV/+xqB25GgxvIxr1iJ0ExbE06uLbkavB8E6oYTNbm6zpwPtA24/mY3hriVN4c4MDf+F9gOKHlh4QxfDWkkyF3pSkObKmCS9Kw2di3MvH8KZ/M8uUZdzLx/DuAQ0vO9UVM6rDu/vEef1qb5bbUrtA7j63nNtmcc9KZFzjw7D1NrAJeNEr/T7tXlxM/owWFvSeKBJvFhbEKv5fXIR1+KGd8qAf2z9vb1lAKTCN4d0j8Ibr69XpHdUhXbcRDochvtCKaLA/GCC9iwcPLi5++MOLi/v3Ly4eOAA/8/PzQB5s2b9/YT/KwQW9inJA/JsXjIo1Aff+eTxmcWF+EdYW8O3++YX5z/X7IQiaGSLKQaANr21GuwrO4L399NIjYn336NLSMqy//MRPijXchdui3Se/CAs48APPLkc3lvD4VJsT2TINeFfp4FKEtYfFNgIh+BKEYUDwCgGQFgSphKmg7eDBeYRvQYBsogrgLtgbUBZp30F9FDCtZX7/gZ/5XNAfDobrAPBgEIhPDnaA4Y1MeNeORZcORbc/c1a63d2jx6Ibj14Wa2obbYADbwi+nzkPx6fbnMiW1sIG4wCCt5D3UB1ARyG8/T7Bi54XjoAt8DMPkM3PS0K1dxVIW5jOp8mtIj/8N0UT/cF6sL7eHw770CbDKwWc6hL4UCAUgBQiFjJsiKMH2gYb4CARNsDxmW0W96xE2jpdRJzUIJdaAG/MLR21GkrPK3xfqD8HAC7gS9TiF/+8gpfc73i4JmQePyOB6AksIVShWLvI8OzPsjdXo1hjDM8rgoGlpYcFkGuAcgJetS3esAzYP2zy6zW80luZGjqMTAshi6ElvISvAMYi9qQXpa08LxAWif8QOsyLeFfD2wC4+w/88Lxooz8Q0UIQwaeF2hTNFxie88H05moUa4wDr/S6Ty+rsEGzGm8zPC8cDEFEqs2JbKmuoKgr1SA/a3pec3v2ZUZk1cCM4MWwAcNcFVP0pYC3BYHBGq4b9NXMr6boFb/rfkQ2hJghhEEitisWpuGJj+DMwYuhLIa40e7jZy144236wKVlANchvPr6lGmE5vBMD7/UdvMyxwQgt3pgFkUDwTLAg/DqcKJPFPUh2wAeFzNgAK92uwv70zgqqDO3Jh2v+HXv+dNDgHcA5AoRPaNF3zDc/GgmTBnr5I6t4DLbAGHApSXIJdx97pGX4yiBtil47z6H2QYMJYxMmt/wGusmvCax8YHELcS3GF9S2ABv8RtbDdiQZIgjII8rSAXPi8gavncyeEHe8ysDiBv6gfiYELyiJyJ2CAzDzaCoQPYcvBPLf3YDb82wwQwSrJjXOC6Gl8ZlsfvtD2QIDDtieHHw3+9DjhcGaypdtliCY1V4we/O7/+D98voRCAr/kMnMfqNijxvtjC8Cbn795JtmuLN6TL9bA68ybABVRBeDHcpWgDHp7MQfYojVNiAP8DcosQ3Gg/eA3Gid2H/wv0/MujHEkDMvb4uYgjT88ppi2ZOlXEi9jq86TZNmTa8ueleM3gYpbakjkZPhhkpwKL/ogh3Y6AxkpCTBQgvzKrJTIP0wCSLY8CbyFLMH3z3/TG6FC+osCHUWbuKczQVT2782xqF99bhubl7X4iiO6fm5h6CDStzc/e9iho333/aOLBheNfSk8FK4oliD+BVpz2DYWNTRXgpkMTUAoQNFALDLBtFEv0QMr8q24BTxALXBT2zi+jVhncxsWV+4T2G36U4hZoNV9dh4iIssyXnVBVIO/DeOrxP8CpoXdkn1gW9Kw/h+xHgfM9p48hZhZdiv5JrmRE2ZKWaEtkG/RZX1GANnCE6WahtkOFDwpfW87xJ+fA//s2+bKwfSHjDEMIGA17d/+KJRqdhw3UAVLhY9LIb972KS/EiNDbe1YTnleUKmHfY/cizKuEQrf0UFjVEUXaVg91mNVtyZCrwpqPdpAIeoUZoUfiirMmhSQqMJAgiHDkBY9H8fAQALyQmhTMZnasB74H7/5kKdpXnjWiKGsxIxLqF3yaF0xo5Z6quQpmGig8EvNeFEyaNmw9+uRheCDikQNyRLbJcAZzpjUf/51FZlAPwUv43ijKrHCJZLj5yLquvvLKKy9UqRxVtwa1NlHVnsTs3lwfvxJJlR4GJ05af/8FvweK7H3xs9M0f+s7c3A98fjT63icee+29nzIOyvC8G/nQanipXCFO/Wp4l43QIVXlkPjAmOLNgM2U1XRiKUuPAl+saHhRKeA017qcLKYoOIpwZg1m2ITntVicz4l5SzyvUVV2cH7hD9GAbRgnHGS7EDbgt0F++GNaI3rt3PNuYGwrRmwQ+s6JGFi439HGvgoDthXppwuEyhV2n4R5MxPeb5zV8GZVOdhtVrelCYUxNDLgNURffz0wo8IcEspUhXFtZCTreWXGwYax/oAtIQuLB979oyHFvDQ61BPFCt4KgzVV2+n6amzMHZFr1+99AXG8dfjIaw++0Fi24YaKEBKelzxsbpVD1CV4C4sh9U70vDgpnGgDUmXILpbqhOh5cbCWTnRNCu/BhQ//ifsH/eGgP5AzbOIDE8jahjAMK8FbeTIyJc1ejZU4pyCgFQ4Xl9+kYPZIfNx48MpyBeBx9wkYrEEccUkM4dYOmbFEssoh0WZVWzKlhRm2dBvpL9fMSeJQFUJitkFv03PHAeVcQyxGF/ASrvubzDYsLiz+qBysBViSg1N8lPEdyeFk6QRFNXizIo5G4ZVx63VYCGhp+aHTjeV518xsA9B6aWnpTz2D2QZZ/ZhZ5WC3WdGWbGm+tqFKG9nlOSiARrCqSgd0qgwnKbBKfNCH9JjyvMnQYUJ4RTD9OfS4AyxD15MjEl7oStWwoeRUZf6aJuFVgN45dQRj3TunHsKsQ+uTFJXEKbxj3IBpqGSnyvSaAa9ydeh5pU/ESQqYYQN4E4O2ieCdFwO2/b/w5+LRGvUU7gaCsIH8fo07oJzCu6HCA5hhg3hXLmcRXtud1LsBMzJUMkvSjcAXR/Y6bKAqBxlO4GytvA1I/cgKs7Hhlfddqg0H9x/44z8Tzw3ThDBGumEE8UMy21DF8BxpPWyorDED8FoaBfPCeW1Y9bwYN8Ycy31hlBywwZCfsg3yjgZ1JwWIKotsqqpsP2bNZGkDhg3gdkM1FQPw1vnMOh+wVdWYNXgVeNbFLHnggVEkqcNYNUmnCUZXS54XHa5Ks2bBC743NT087owGJXsX5ve/+zflDNtwGOD/UHrekbypo8WT6wm8lWbYsqTyYxt8SJVZ8OaGEulpDSp8xMRCXNdtzAcPgiCuNMPjZIGMhlcW5Mzb8M5nrFUVqTH/uV+RUxPr6wFGDjA3DD54EFQrhMwxvBUFzzyvB/DWqDZRJYKFFQ7WgI1eEdtA37lGnhfzvDBMwpJIem5DiAkyWZgj7yfDajL57Ib0RMWk8gt/huCNcFZE3kmBeTP5zJP8MtDkjlmDl54+QvNpVjUO1O2cpzKdRJvFPSuRDIVqNWIZGsVhQ+oeNlXVo7bK2l34cu4PwhhemXGgsb4qRsd4Vz41R3tZ4xbiA5lgFuwlBy52/LG/BTewBWFfxgzYNnj9AUY0+acnvaPT8G7MzR3ZKJ8ktsS4E9Oqxtk9uqzKdBJtFvesRJqEt1ij8AbMeMCGCapVHQwHqkpSuWFBEN4GJMIFeMQYPDEH7mE7cGA/3UsMsrCQEUCYgUSqince8haI8PyPwBNzAF743MiviQBLIktK6PYWvCv3/ffDR+6c2lfrd9sPcIjnhBW08o75yI+woY5GwXMbjK14O8XI2qaLZ9WDywS08qF4tFxYGM0jygv4yCcVSkDZ5MK8fLCTJBvfCdeNz3lSKEf4u8RWoX/wD/8RiHDVfZ+qXfHZGZkxUrERtuHVxR94bx0+Iv7T9Fx1ScErq3EI3jXjeWUeDNimrKEfGRlFkXwspFwqBXqnHv4Y0Rv1hEh4Et+ierTkjy0Itw3czi/GR+KLutsnTHxFTMHwPQev4XllmU6izeKelUin4HXaxN41IzNs2ICwAW8fqiHyOXuXdB2kinlV0dnj6pFPDO9Um9i7ZmQP2K5Dmrceu8LJPnKeynPsahxVt+PVbUDeaHjZqa6YMXMzbJ5peNmprpjB8LrV8LJTXTEje8BWM2KoKQzvVJvYu2ZkD9jsuy2aFoZ3qk3sXTPywoaVuTnr4SRNCsM71Sb2rhkFMe9KvTxvdWF4p9rE3jWjwPO2xS7DO90m9q4Z2bUNLcYMEcM75Sb2rhk508O1f3MdYXin2sTeNYPzvG41vOxUV8xorp63ujC8U21i75rRXD1vdWF4p9rE3jWjuZLI6sLwTrWJvWsGw+tWw8tOdcWM5up5qwvDO9Um9q4ZzdXzVheGd6pN7F0zOFXmVsPLTnXFDIbXrYaXneqKGc097qm6MLxTbWLvmsGe162Gl53qihkMr1sNLzvVFTMYXrcaXnaqK2YwvG41vOxUV8xgeN1qeNmprpjB8LrV8LJTXTEj68+33nMaE2Y8w7Znr3rjGr7AC38xHsohubZh7171xjU8gRf/ZNvN9x3Bv9tWu4FKwvBOtYm9a0bGDNsR8r5cErl3r3rjGj7Bi06X4d2zV71xDU/ghb/4SncArXDY0KhG/FfUPOpUQxqewAteF0Pe6609rmy24NXIflaLAXFnzHDeRLVU2Qpkye6cau+xIzMAr/HHKj+bLyk/3G6nWtTwBt7WZe/CW4VYW366NsFeGs7w1hLfriEx+9OVqdXwGk7YAzPG1GB4a4lP1zD2tWPDqwh2acYEGp7Ay3dS1NOwg4TJ4K3Ery+GT7sJ9rzNaqSj24nhJX6LAPbBcBdNMLwNamQOy5qAtwRg54Y7aoLDhmY08hMKTcGrAM44VQyvlnzPu9JaTWSH4S3LhDUJbw7BDK+WPHhvHZ7lSYpenkzK4ngKFsAMr5YceK/PtfZ0Xn/hTTFaG8WW4P2s6YEZXi3Z8K7MtfVsXrNNU1yfrky36hG8KMgvw6slC947p9r8E4Lewdvg8KtleD9bmkdr+VS5bKIavDff11aaIdGmKY5OV8PDr/bhFRq1+Z0heDdaDRnMNk1xcLrKB2B+wgsvtQCeHXhnI89brfLLX3hBfCrl8QTeKYhbeGuULPoNL0ilekqGt0FxBW/NUtsuwEvSK2Z4duDVYUN7o7bpw1sb2wbJml4TcZ56wnPbWXi1XG9tim168FqTDlNA0S28sZiTgjMJb7TSwT8iWDSbO0PwmpI71V0QanQfXhfPbSg+0yzeiX35asgehLe6eDlLymZMVYPhLdI4cwYW9GpIaoPeiG089VTWL8vaeuYMNAGaBW0kdokmwrCk47bMIrxdjHmzJAhwEa6a7/p9Y2G9OX4cXk+cEC9/4+RJ4Or48TN6O7yaG/QLbYS1p44ff4p+gd4pfnCrXFc/Qucp/H8GtAWkQumppxDWMydPHj8B62eeOnnyqTOC/H4/FNSG4SgMhoM+9jVEMW3FdyEciIbSzlHygKSkNnYe3g5mGySY9tsgDGEZDvpwiYJ+H9716erTwnpz/DiQdeL4cQHfiZMnT6gtxmtyNesFfoF+V1cEqydo7eQnP0kr/+BLoneBgHbQB3JBwmB92O8P18PY8nA4FOCurwfDYV+YLdZC+1TJLbakN3YVXp3n9bEY3XARGQoS08TbIAhSuJbCayI8FryKw7HgPX7ihM+AFoAAABmBSURBVF4leJ//9V+H7g37hqyjAxbbAjQVnDCaLFgU5BbAC2dR/eiNY1yNSRRmb4bNPMsV4CUnG/bR5Y4DLwUAbuElef5n/00/KcOh9MHi0ylgBScMHjeAaEJ4YBk2iP8j4zMfomcO1Y88U/Y3FsNbIG3Bq2JXKRJeifQ48Oa8tg9vSj7+D/+pBe4AHG9/Xb0V+CHOYQAOV7zXuMIpW7U9awreveN56eHSkGjwINuQHEkUhw3x1cCFjBfCwWBcz+sPvMd/1YYX+B2iuyXfS70fwOc17A8GfX0SEF5xBqwwYdywIT/LwfCmJHN4ka+gDsdhi6ZWvmsB3mbT/YXsPv9zqaihL6wLFLwQ5AK3wgMH8BJHt+L/QGwQJ0HHu/p8hcmVwqtRdDkY3pTUhFddBJVfkAy3B+/xfwGruS+0UoKlksKjPvmln03DKwZkwXAgolxwtUOxOsDxXEhBPpgtTR/EwzfzjNZmkeGNJgkbShVIy6J2LHhz8gyu4D3+8b9MA7ZhYNAbrK/D5xQSZsLpwlgN9waALY3Z6FM8EjTDSQiCqARelSbOObmpgV2Vq9Giht/wFkiRQmhFD3vA84qP0sd0hKCiBvS4kFKAbYNg0CfPCyECYA2BLpkOAzaZZbD40+NbK/gFpXLPm3ArDG+tec9y4+3Q16bWmqvoArzHT9gxg8r4huB08T1ECwT1QJA8gASaPJ8y2xCquFeKzizaaQfYPMq8EvKEWiqVr0ZS9hy8RTFupkKhyHOtMmYZU2vdhFciqmUwkPHEYCjHbTRw0052FH8TmQmZNLyUgwDPmz0TR24g60o1C+/KnHrmzc33n9bLkfGepN4NmDeWlpYOFbS6+8T59Ma7zy3bG5qD10z+GCIvluVyg2DQZXh/4nnb8/aHAzP8JZjDCGYsgOYQEmYq2yA5MbMNOoRQUa599rLhNSeB2gwbVh5Sf4pK/WUUXI6i5F9KqTVJceOR8wLFY/kHTAhvbtiQsR0UdG4snYWPYl8r056D3Im2DsD7/N/52za7g0G/n9oCRgqMcXpN+lkR/YajMDUlnHfm9ICtJGzIvnx1JF8DvSu52I13kafF5Sh+L6UOvETh7pOXdz/y7CPnd48uLS2jMxZIywXAS9t3n/wi7r799NIHnl1WR9ltVrMlyvbI5fBKJuW4JBy+6ATeuWbg/Y3fSMJqUKxLdVQoDNkGeCV4V2mhpoazIDZOHLw4naS4rh+Td/PBLyOstBzp90rqwKv96u7R5ej2Z87ChtvPnI8uHYrkAjbQ9t2jx6Ibj16O1sRiaVnujuStEqMyWV21377yymrhcWrZ7LRB+bzC3wewcl9ARPRVEd4pS9aZxvOce7JTl6Ul+eYPfWdu7gc+Pxp97xOPvfbeT6WXWmrB++Rlm2LBJLAKa7SQO8R29MHEtnDYcnfiA2OK9UFMedqMaeIKAzaqyJGzxFEwgNeOet5P/3Njejjoa2cr417Mi+noVzjj9XUcsJHlI8o7qGmLgpEFzafnndwpTVLgUx7B/W7so+hBLkfqvZa6nlcEBQ+fJUbXKFKADZFc4A7cLuGlDcvqKLvNXFvKBm4F32zmUTLbIEsc8ovLOhDz/uq/ToYNZr63PwxC6n9AO0ICV46wkqmy3G9/Ou/GEC/M2J0pjcILYcOtw0duPvgCwqqWr8llLPVjXuFFAcnbTy8rRwvhAS3A19J22/MaR0VVYt6SfG8evIlRs8zzjl8S6QzeVEnkl34O4R3G5EbyvfS4YaQyaHKSAj+3WOEbrRqzNYVnNhHzapbLL0uT8G7AHTwC3g3KeenlN+UyPrJetgG85w3yvPjz+FlAUvzIhaL2cX0Ixbxyd6LNfFvKJiuyv9kSo2GZKrOL0SveSeEY3rR8TDlZEijbRXYHcpJiHe4NGgTrw8EwwEkKWViHVWU58KbPMg7lbHirpS8bHbBBivbWh07Des08b6FAJkEmFaJLS5hHoOBBxRBiB21X8N59zjzKbjPXltwTliyJtE9/nHUPjbft1fNOEd6f+IsfM9gdDsjhop9FeAd48wS4Y2EtzVDEMxMKXlWYk8o02DULdthQCK8+/03Ce+fUQ/jn22G9SXibkbHhNeeHRunj1AyQ3Bxq9wOvmfAGuhQ28hveE3/2x814Nx6c4TqQGg5lZSTCG0KhjnK3q/GJMCvL9NkLgmTFjXk1Cr4F4/Pf6CTFnVPjzrBNQcYOG8zTngWvDBuK4M2KgDsAr/C9/WwZiECB7hbGOh3hgwOcpAiotgHOxcAYsoah5QJQ5DRO7tXIlZbgrazhF7xlM2zGF15G2GC4EnoNJrwB0yN4/0IevCLQDbE4cn2Arni4TnfD400V4oQI4yW8mtHkWZbjgNTVsM990XVheCuEC3pDdipSsS1HavmlOB7eSVHM7l/5Sz+eplZHwEMYpuFYDSp86VxghYOcYcMCXz3jmM6ap6Z9jZM7/QFbdQ2/4M2ZPM+bHs4R6WJseAuKy8aFV63mvVgrE8hTx49//M9/2gIWJKBc7xDvxRyEQVwDCXwaYcOIFqp+N302i24DYniz2zSlEN68wpwcUfBa35YteN5pwXvyk5888emsiEHe+45DNkHwUMeyiVvf7aFuNrzWKU6HDSXJS4a3qXpe+87LLtw9fKLgnfC8v3zyn6iKSMwvyBvdAzlrgV42hpceOhLE5xPHtkZ6LBU2JG5uyzi5ZVeG4S2fnEgpZEsYphd2vGAlHXLhNR86Qs8ik08cK4QXj5QQniiF9+RJ8f9M3t5fPgkNnjnxa/9ITQ8PVrGrAC4yi/MVId22pgEN4sxuVB4EMLw1xM1TIqkwJ7IHLWbRpFwAmPJ1RE92pC30sEbcYhyS9XJGPYYPn9ZHoOf9jMQxZ+gJffiUPXrQHm6EpuhVNPs83oUGcw/0HYW3S+AUWvpbPQztz21p7qAwbCjQK1IoE4a3+xpedqorZjC8bjW87FRXzGB43Wp42amumMHwutXwslNdMYPhdavhZae6YgbD61bDy051xQyG162Gl53qihkMr1sNLzvVFTMYXrcaXnaqK2YwvG41vOxUV8xgeN1qeNmprpjB8LrV8LJTXTGD4XWr4WWnumIGw+tWw8tOdcUMhtethped6ooZDK9bDS871RUzGF63Gl52qitmMLxuNbzsVFfMYHjdanjZqa6YwfC61fCyU10xg+F1q+Flp7piBsPrVsPLTnXFDIbXrYaXneqKGQyvWw0vO9UVMxhetxpedqorZjC8bWjgQ0vbbcIvDYa3lnh5DUEDuH0LpBLA3prhXRMMb+saElwlpfx6aoaHTTC8LWsk0CV8XXeK4W1S9iq8At3/k4K3BF8PzRhHg+GtJd5dQ/S6WfAW4uudGeNpMLy1xLdrSBFDNrwFoa9vZoypwfDWEr+uoQp2c+DNd75+mTG2BsNbS3y6hj09UMuFN49en8yYQIPhrSUeXUMjx5APbw6+HpkxiQbDW0u8uYY9Mz9WBG8mvt6YMZkGw1tLfLmGdmq3GN4Men0xY0INhreW+HENk7MSJfCm8fXDjIk1GN5a4sM17KVm1ErhTeLrgxkNaDC8tcT9NUyjWwleO+vr3oxGNBjeWuL6GmahWxHet4yKM9dmNKTB8NaSVk9Xz5Ts/dlQVoVX8lu57Hc8M6anwfDWkhaztj17vsymzNg9GbxSofBDMlXDJ9FgeGtJW4kvE0wbxV4xt2PCm/j1rgyfUIPhrSUtnK4UmrVRnAReTfD0DZ9cg+GtJY2frnFzB43CW4FfhlcLw4syWe6gUXhBivBleLUwvM3kDsbVyFPoWAUww1tLmjpdRYMwh/C+lcsvw6tltuEtSR+4hfetbH4ZXi0zDG8Lia+m4c0avjG8WmYU3nJwx0KxeXhB7AQww6tl1uDtVZlqGBvFduBF0fNwDK+WWYFXT8K2imKL8Copqbpo4FSNo8Hw1pKKk70Gs1NAcQrwmhq96tL8yZ1MgeFN/+aENMdJWxrT6lQ9nhneXGkQ3hq+hyVb8k9u7asxbY2Ow9t5DS871RUzGF63Gl52qitm1IB39+iSkGP5+584X6R+97nlRJvFPSuR2grhal2NkjZ2dso08Ah1WNbhbZoRhmO3sRfhLYbTc3jD4YthxtaMbak2srHbefPNnQSfUkNt23n77R15WERv0r9tlPv7syVcf6Wwy8aB6+rAUbGVGbJ34b399NIj56Pdjzz7yHlajW4sLcl1XKOjhZ9ejnaf/CIsQOcDzzqFNwhXg9RG4wprMVCiNiR2ScS2rl7dknziAbh/pI4HZnFlS4jxW2KIqa1RpPGuJOPAu5phZbHsXXjXjkU3Hr28e3QZVy8dim4/cx4WYr9cE3L7M2dhw+5RPBh1lrzwvJYXyoDXRGlEPpTYNKEDuXrt2lUDXto/Ur9ha2dnC3lGxkGAYeWO47auRHRoZQkGUcqMbIP1EQyvjHkJVYEmoAyEirewiBBuuSZF7IKjJNR+xLwJXNMMSNjQXV65dm1Hraf8Ix24ow4gKK/syCN3Njd3Ynh3DFe8s6O9uMD2jfRvzuGSNkvPm/WVkSscNmjPC4u7XyB4nxY4P3wWsH4YHa1cQ1mDCELCi6prbuEljaKrjtcYHWq0tbkpgNx8882r2vMm4d0hsiMz+t3538oHX93cvEphg4AXVsC/WqEDAS08L3044l5k9xA3h+FY8FY+cmyNrsBret5n9BANAgnaD1ECBLnLilpPPK/UyPdCBATFqJtCouiNrdinKvCamBaA3yUBHhnR8I7Rjcze4c9qiRl5htfR2bPwqpgXnSmuAq60Ra7Jg3cfPyvh9SDmLdeQ1FwVzlbCO8J1SZVkq/eOlO2trW25dvXq9jvbb721/c61zc1r72z3erRG78ROODQ+vGfGviKsVgNCWmxt5YcN2MOxDa/hrT24GlU0xs82wDuxClECRgjCtT5yfk1nGy4tQX5BwXv3OdfZhgoaSI30sxCyRleSg7TIhBdofWd7W2KML8D8O9d6PWIW8MXtcKgFLwgN4KCJHSMNUTx6gx4yvFpmfIYt7eWkA0SfeyUjSQbwbiOGMbeAcQzvZq+3ifCS58Wd2+SeE55X/LwRxwz4n+ANAt2zVA/HN3yWw4bGxAd41dgdnZF1VSl/gAzJVJkemYH03lEcArzXtrevSYw3N7fpBeG9JkSwLA4TSxA6kuBVMa/4uSLdLo35xDb41ARh2JduMu0ueXpYy+zAa82rKmpxEQ6HSZ8k4cX1q5hzkLIj4N3c2tp8x/C3CO/m1ati4+uwhvAKr0tHYHShfDQc3IuMlNnIbE9s33r77d8NW4AXPp/T9bx3Ts3NPUSrN99/Oro+h3Jk7CYmgVclvGpInF+w37UOb9ZVsqemFK94ZNDvG5Nv5AoxfUVt6GkG2Pk2DMbAq5oxL6wR0d8HHwsxL8KrYwoZZWDs+1Zvx5xPlp5XZZR33v4v/209bDxsULmK6hq1m0jKyr7o1mGk986pe07Lbfe9OnYTMwJvhidN1jZYh/SF6DfmDJgMG3Dwpvb2tlUksL1tDd0wRgDP+05PeGcNL22PExQIbywqrBZhwxbmgXd+N4sxw3F2BF7wttEGwrrxLlgXcl1BPE4T48GL2QV4efmJn1SFCqq4AZHcffwsJMhkoUO8gPyC/c5us7hntU3RIr51M8oYbHjNC9kfDAx4yQcitlcwEpUJAOkidbbBkG0VAm//XwgUenPbGEUgvHGsq3JqlvnUBHxkru7sXMU3YfY0turyqFaOlzRchA0gCO/NB79M8N45tW+CJsaDV+d1jUIFKGU4ijBeEm8E0WKxFpcxxNUMdGyc58Us/ahdWX2xgZmEXPl+try+tfU6vVwVod33YYbt9c1N8e7116+qY64K+f732+xcr/WTW0e++8HHRqPvfeKx1977KXj7nT/wqWZ/fzm88Yyami6jigY1W/zk5W/84qG7XzgrjzMWwiur6ofpzrDZLka+KyiENVMPcnoY59beoPIFnQnAzpsBg4wgdN6BfG1v7homzcjpqrBhWwbLyvOSJ5flEzps2JLNZNRgqLChdIa7zqnKlkauhhixgePd2EchBEbBzXaqHN64lkHBSxUNerb49555+YnfI5hlzYNcRGvL5rGOahv0ta42w0bDMwmvMT1shA16DGYiSy8UNvRo/GaFDThYw9m2nlnfQE1gaIJ5Xl1CkfsdP4IscJ4JWdG+w1TZ9XtfuPngCxLeW4ePlGvUamMcz/uMLtABWfvrH737hV/U1WYJz/uMrodwVdtQGV45w0Z+Fl+vGPEvCQ7YTHjBl8bJ3njAhg4X3DKlJiS8guZr13q6vlf8bMqA+s03t7Lrf9KGF3jezGjfIbyC1w2dIhMkN9ypWjGvUaig63BELEtRb27M67qeV/mwavCalQxXjDwDyc6bvW0zbKAhmE72IrLoeeOd+LKtU2UKXjnLdmVLvdPRye+HhUOrIng98ryIqnS25Hk34jxZQ52qmm2AyoWXJbxU0aDgxVyDnWaAhaxmkNUPXahtSEOxaU5PoOxc68XgbqeTvZtX5fQwbMPUr6pt0Lneq0aqbGdnpN269Lk7vz8cBkVJrVEY5O/0J+a9cwqcLfFK8FohL8+wNauRhvcNY3qCZGunpxJfROQ16+3m1WuqtgGcLNY2KHilu1Yxr5RkYY41N5wpdF/EOCWRmUZn/p6mZtikr0V475x6aKImGF4tWRctSM6zXbGqGnDzmz1raiL2vHGBg64qo4gBax6MaeLUJMVWop5XfPUXoYnwNlWMnvN7PHAlVTRmFF510UxK5DbjvrWMksidRFUZoomFOnbMq2sbsNBM1TbgGM/+28M7+uagHTltnJ9MIA1Mm5XAa9HP8DYo/sBrDXBom5ltGO2YLpFk8lkE6wMxitTQTTjgLfD0ZVySGcVhg/073IQNU9BoBd7MIghPbgOSQhfNTi2hy9syJiZII333WrwBxmSUFEb/ufmGeEu3FW9uSpcqVuCAzFvf6fOh4cUBYjV4LTNSWxLw8g2YNWRq8Na6KhlNZHhemmEzPa968oIWA14q2tlStQ/4FmaGsUSC4BUrWFSp7ho2/fjWlop5qRIdWyoLG8z+p0DPqlDmW9/riLyPuPWHjlQbthTleU1QzBk2OUlBd+ak4I2n3LbsfMTO/3uT7hc2ntsAc2dy4Jd89oPYtxkP2CL6FNTxvLnwWsLw1hFZANH6Q0cqwVs0w2b/ApphQ9DMAVt60ise0121M8F06zs+t0Ep0Tu5nob3jZ0dO6ZuJGxIaHDYUEPim+RbvvW9ylWpDi+KWcmQNWCzD9xJJNOuaN148sE4IPXIqK2sThUb5YYT901MFV5vHjpSFDZkgJJ6VlmeJJ6xV7NX4yr4qbG34PX2oSOeaXjZqa6Y0Ra8HXzoiBMNLzvVFTNaCxs6+NARFxpedqorZszoDJs3Gl52qitmMLxuNbzsVFfMYHjdanjZqa6YwfC61fCyU10xg+F1q+Flp7piBsPrVsPLTnXFDIbXrYaXneqKGQyvWw0vO9UVMxhetxpedqorZjC8bjW87FRXzGB43Wp42amumMHwutXwslNdMYPhdavhZae6YgbD61bDy051xQyG162Gl53qihkMr1sNLzvVFTMYXrcaXnaqK2YwvG41vOxUV8xgeN1qeNmprpjB8LrV8LJTXTGD4XWr4WWnumIGw+tWw8tOdcUMhtethped6ooZDK9bDS871RUzGF63Gl52qitmMLxuNbzsVFfMYHjdanjZqa6YwfC61fCyU10xg+F1q+Flp7piBsPrVsPLTnXFDIbXrYaXneqKGQyvWw0vO9UVMxhetxpedqorZjC8bjW87FRXzGB43Wp42amumOEEXhaWRsQBvFmS5Y271wSb4aQJhrcrbbAZKWF4u9IGm5ES1/CysIwtDC9LZ4XhZemsMLwsnRWGl6Wz4g7e208vPXoZ124sLT1yvt0mdo8u6T8+31Yb9IfDW2zAbKqtNloyop1r4Qzeu88tR5cOwRr+rfhDrTZx+zNno93Hz7baBnwIW/gMxg2YTbXVRktGtHQtnMF7+5nzxoe8HZ+lm7gBp22tDddrmLH28C+1YEXcgH3G2mmjJSNauhbO4N198jJ+CEla8Sh2E/Faa220gVbcgG1OO21EbYUNrVwLZ/DeeDS2Zvfow21cE7MJ8b11rIUm7DbauO5xA1ZTLbURtQVvK9fCCbxrS0uHWnaLySZuP908uykz2PNWaqKxa+FJzNtKQGo0sXu0nVyDbUYb133KMW9b8LZyLRxmG47JSLe1r8O4idbYNdqI2rnucQNWUy21EbUFbyvXwnWeF/NkS0utxLxxE6KFpZYSvbEZreZ54Ve3nedt0Yh2rgXPsLF0Vhhels4Kw8vSWWF4WTorDC9LZ4XhZemsMLxeyMbcQ7Ry60On4fXw3Ny9LzjsTzeE4fVB7pz6k5LVlXtOA7v7xNp9rzrtUweE4fVBbj745fcdidDjArzX4eXm+0+77ZT/wvD6ICv3/Y9TwtlG1/fFyDK8pcLweiC3Dj8UbVDcECPLYUOpMLweCIQJNzFuiOHduOe0uw51RBheD2RlH4zZIG7Q8G7MHXHZo24Iw+teIDE2J3NjEt4V9rsVhOF1L9cJW4wbCN4NTvJWEYbXuciAgRYILycaqgnD61zkUI2GaMjtBoYRHPWWCcPL0llheFk6KwwvS2eF4WXprDC8LJ0Vhpels8LwsnRWGF6WzgrDy9JZYXhZOiv/H80/m7EXMaFhAAAAAElFTkSuQmCC" />
<!-- rnb-plot-end -->
<!-- rnb-output-end -->
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" />
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Calculate percentiles:
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<!-- rnb-source-begin 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 -->
```r
percentiles <-
data_validation_GPS %>%
group_by(EUNISa_1) %>%
summarize(
perc_10_A59 = quantile(A59, probs = 0.10, na.rm = T),
perc_20_A59 = quantile(A59, probs = 0.20, na.rm = T),
perc_80_A59 = quantile(A59, probs = 0.80, na.rm = T),
perc_90_A59 = quantile(A59, probs = 0.90, na.rm = T),
perc_10_A15 = quantile(A15, probs = 0.10, na.rm = T),
perc_20_A15 = quantile(A15, probs = 0.20, na.rm = T),
perc_80_A15 = quantile(A15, probs = 0.80, na.rm = T),
perc_90_A15 = quantile(A15, probs = 0.90, na.rm = T),
perc_10_A12 = quantile(A12, probs = 0.10, na.rm = T),
perc_20_A12 = quantile(A12, probs = 0.20, na.rm = T),
perc_80_A12 = quantile(A12, probs = 0.80, na.rm = T),
perc_90_A12 = quantile(A12, probs = 0.90, na.rm = T),
perc_10_A60 = quantile(A60, probs = 0.10, na.rm = T),
perc_20_A60 = quantile(A60, probs = 0.20, na.rm = T),
perc_80_A60 = quantile(A60, probs = 0.80, na.rm = T),
perc_90_A60 = quantile(A60, probs = 0.90, na.rm = T),
)
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
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<!-- rnb-text-begin -->
### Get filtered data
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
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<!-- rnb-source-begin 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 -->
```r
data_validation_GPS_refin1 <-
data_validation_GPS %>%
left_join(percentiles, by = "EUNISa_1") %>%
mutate(EUNISa_1 = as.factor(EUNISa_1)) %>%
dplyr::filter(
(A59 >= perc_10_A59 & A59 <= perc_90_A59)
)
data_validation_GPS_refin2 <-
data_validation_GPS %>%
left_join(percentiles, by = "EUNISa_1") %>%
mutate(EUNISa_1 = as.factor(EUNISa_1)) %>%
dplyr::filter(
(A59 >= perc_10_A59 & A59 <= perc_90_A59) &
(A15 >= perc_10_A15 & A15 <= perc_90_A15)
)
data_validation_GPS_refin3 <-
data_validation_GPS %>%
left_join(percentiles, by = "EUNISa_1") %>%
mutate(EUNISa_1 = as.factor(EUNISa_1)) %>%
dplyr::filter(
(A59 >= perc_10_A59 & A59 <= perc_90_A59) &
(A15 >= perc_10_A15 & A15 <= perc_90_A15) &
(A12 >= perc_10_A12 & A12 <= perc_90_A12)
)
data_validation_GPS_refin4 <-
data_validation_GPS %>%
left_join(percentiles, by = "EUNISa_1") %>%
mutate(EUNISa_1 = as.factor(EUNISa_1)) %>%
dplyr::filter(
(A59 >= perc_10_A59 & A59 <= perc_90_A59) &
(A15 >= perc_10_A15 & A15 <= perc_90_A15) &
(A12 >= perc_10_A12 & A12 <= perc_90_A12) &
(A60 >= perc_10_A60 & A60 <= perc_90_A60)
)
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
#### N plots per category
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
<!-- rnb-source-begin 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 -->
```r
bind_rows(
data_validation_GPS_refin1 %>% count(EUNISa_1) %>%
pivot_wider(names_from = EUNISa_1, values_from = n) %>%
mutate(data = "data_validation_GPS_refin1") %>%
select(data, Q, R, S, T),
data_validation_GPS_refin2 %>% count(EUNISa_1) %>%
pivot_wider(names_from = EUNISa_1, values_from = n) %>%
mutate(data = "data_validation_GPS_refin2") %>%
select(data, Q, R, S, T),
data_validation_GPS_refin3 %>% count(EUNISa_1) %>%
pivot_wider(names_from = EUNISa_1, values_from = n) %>%
mutate(data = "data_validation_GPS_refin3") %>%
select(data, Q, R, S, T),
data_validation_GPS_refin4 %>% count(EUNISa_1) %>%
pivot_wider(names_from = EUNISa_1, values_from = n) %>%
mutate(data = "data_validation_GPS_refin4") %>%
select(data, Q, R, S, T)
)
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
<!-- rnb-frame-begin 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 -->
<div data-pagedtable="false">
<script data-pagedtable-source type="application/json">
{"columns":[{"label":["data"],"name":[1],"type":["chr"],"align":["left"]},{"label":["Q"],"name":[2],"type":["int"],"align":["right"]},{"label":["R"],"name":[3],"type":["int"],"align":["right"]},{"label":["S"],"name":[4],"type":["int"],"align":["right"]},{"label":["T"],"name":[5],"type":["int"],"align":["right"]}],"data":[{"1":"data_validation_GPS_refin1","2":"3126","3":"5189","4":"1894","5":"281"},{"1":"data_validation_GPS_refin2","2":"2677","3":"4455","4":"1609","5":"241"},{"1":"data_validation_GPS_refin3","2":"2294","3":"3641","4":"1393","5":"206"},{"1":"data_validation_GPS_refin4","2":"1911","3":"3102","4":"1233","5":"181"}],"options":{"columns":{"min":{},"max":[10],"total":[5]},"rows":{"min":[10],"max":[10],"total":[4]},"pages":{}}}
</script>
</div>
<!-- rnb-frame-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### Split into training and test data sets
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjMlYwTG5ObFpXUW9NVEl6S1Z4dVlHQmdJbjA9IC0tPlxuXG5gYGByXG5zZXQuc2VlZCgxMjMpXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuc2V0LnNlZWQoMTIzKVxuYGBgIn0= -->
```r
set.seed(123)
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
<!-- rnb-source-begin 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 -->
```r
train_indices2 <- sample(1:nrow(data_validation_GPS_refin1),
0.7 * nrow(data_validation_GPS_refin1))
train_indices3 <- sample(1:nrow(data_validation_GPS_refin2),
0.7 * nrow(data_validation_GPS_refin3))
train_indices4 <- sample(1:nrow(data_validation_GPS_refin3),
0.7 * nrow(data_validation_GPS_refin3))
train_indices5 <- sample(1:nrow(data_validation_GPS_refin4),
0.7 * nrow(data_validation_GPS_refin4))
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxudHJhaW5fZGF0YTIgPC0gZGF0YV92YWxpZGF0aW9uX0dQU19yZWZpbjFbdHJhaW5faW5kaWNlczIsIF1cbnRyYWluX2RhdGEzIDwtIGRhdGFfdmFsaWRhdGlvbl9HUFNfcmVmaW4yW3RyYWluX2luZGljZXMzLCBdXG50cmFpbl9kYXRhNCA8LSBkYXRhX3ZhbGlkYXRpb25fR1BTX3JlZmluM1t0cmFpbl9pbmRpY2VzNCwgXVxudHJhaW5fZGF0YTUgPC0gZGF0YV92YWxpZGF0aW9uX0dQU19yZWZpbjRbdHJhaW5faW5kaWNlczUsIF1cbmBgYCJ9 -->
```r
train_data2 <- data_validation_GPS_refin1[train_indices2, ]
train_data3 <- data_validation_GPS_refin2[train_indices3, ]
train_data4 <- data_validation_GPS_refin3[train_indices4, ]
train_data5 <- data_validation_GPS_refin4[train_indices5, ]
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin 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 -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxudGVzdF9kYXRhMiA8LSBkYXRhX3ZhbGlkYXRpb25fR1BTX3JlZmluMVstdHJhaW5faW5kaWNlczIsIF1cbnRlc3RfZGF0YTMgPC0gZGF0YV92YWxpZGF0aW9uX0dQU19yZWZpbjJbLXRyYWluX2luZGljZXMzLCBdXG50ZXN0X2RhdGE0IDwtIGRhdGFfdmFsaWRhdGlvbl9HUFNfcmVmaW4zWy10cmFpbl9pbmRpY2VzNCwgXVxudGVzdF9kYXRhNSA8LSBkYXRhX3ZhbGlkYXRpb25fR1BTX3JlZmluNFstdHJhaW5faW5kaWNlczUsIF1cbmBgYCJ9 -->
```r
test_data2 <- data_validation_GPS_refin1[-train_indices2, ]
test_data3 <- data_validation_GPS_refin2[-train_indices3, ]
test_data4 <- data_validation_GPS_refin3[-train_indices4, ]
test_data5 <- data_validation_GPS_refin4[-train_indices5, ]
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### Fit models
<!-- rnb-text-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjSEpwYm5Rb2NtWXlKSFJwYldVcFhHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxucHJpbnQocmYyJHRpbWUpXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucHJpbnQocmYyJHRpbWUpXG5gYGAifQ== -->
```r
print(rf2$time)
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoiICAgdXNlciAgc3lzdGVtIGVsYXBzZWQgXG4gICA2LjU1ICAgIDEuODkgICA3Mi4xNCBcbiJ9 -->
```
user system elapsed
6.55 1.89 72.14
```
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjSEpwYm5Rb2NtWXpKSFJwYldVcFhHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxucHJpbnQocmYzJHRpbWUpXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucHJpbnQocmYzJHRpbWUpXG5gYGAifQ== -->
```r
print(rf3$time)
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoiICAgdXNlciAgc3lzdGVtIGVsYXBzZWQgXG4gICAzLjc3ICAgIDEuMzEgICAzNy44OSBcbiJ9 -->
```
user system elapsed
3.77 1.31 37.89
```
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjSEpwYm5Rb2NtWTBKSFJwYldVcFhHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxucHJpbnQocmY0JHRpbWUpXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucHJpbnQocmY0JHRpbWUpXG5gYGAifQ== -->
```r
print(rf4$time)
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoiICAgdXNlciAgc3lzdGVtIGVsYXBzZWQgXG4gICAzLjk3ICAgIDEuMjcgICA0Ni45NyBcbiJ9 -->
```
user system elapsed
3.97 1.27 46.97
```
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVjSEpwYm5Rb2NtWTFKSFJwYldVcFhHNWdZR0FpZlE9PSAtLT5cblxuYGBgclxucHJpbnQocmY1JHRpbWUpXG5gYGBcblxuPCEtLSBybmItc291cmNlLWVuZCAtLT5cbiJ9 -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucHJpbnQocmY1JHRpbWUpXG5gYGAifQ== -->
```r
print(rf5$time)
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoiICAgdXNlciAgc3lzdGVtIGVsYXBzZWQgXG4gICAyLjc2ICAgIDEuMDggICAzNy43NyBcbiJ9 -->
```
user system elapsed
2.76 1.08 37.77
```
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
<!-- rnb-text-begin -->
### Predictions
<!-- rnb-text-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
<!-- rnb-text-begin -->
### Confusion matrices
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZMjl1Wm5WemFXOXVUV0YwY21sNEtIQnlaV1JwWTNScGIyNXpYM0ptTWl3Z2RHVnpkRjlrWVhSaE1pUkZWVTVKVTJGZk1TbGNibUJnWUNKOSAtLT5cblxuYGBgclxuY29uZnVzaW9uTWF0cml4KHByZWRpY3Rpb25zX3JmMiwgdGVzdF9kYXRhMiRFVU5JU2FfMSlcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuY29uZnVzaW9uTWF0cml4KHByZWRpY3Rpb25zX3JmMiwgdGVzdF9kYXRhMiRFVU5JU2FfMSlcbmBgYCJ9 -->
```r
confusionMatrix(predictions_rf2, test_data2$EUNISa_1)
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
```
Confusion Matrix and Statistics
Reference
Prediction Q R S T
Q 492 287 139 14
R 306 1168 105 44
S 134 116 283 8
T 5 15 8 24
Overall Statistics
Accuracy : 0.6248
95% CI : (0.6077, 0.6418)
No Information Rate : 0.5038
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.3973
Mcnemar's Test P-Value : 0.003049
Statistics by Class:
Class: Q Class: R Class: S Class: T
Sensitivity 0.5251 0.7364 0.5290 0.266667
Specificity 0.8010 0.7087 0.9013 0.990844
Pos Pred Value 0.5279 0.7197 0.5231 0.461538
Neg Pred Value 0.7992 0.7259 0.9033 0.978682
Prevalence 0.2976 0.5038 0.1699 0.028590
Detection Rate 0.1563 0.3710 0.0899 0.007624
Detection Prevalence 0.2961 0.5156 0.1719 0.016518
Balanced Accuracy 0.6630 0.7226 0.7151 0.628755
```
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZMjl1Wm5WemFXOXVUV0YwY21sNEtIQnlaV1JwWTNScGIyNXpYM0ptTXl3Z2RHVnpkRjlrWVhSaE15UkZWVTVKVTJGZk1TbGNibUJnWUNKOSAtLT5cblxuYGBgclxuY29uZnVzaW9uTWF0cml4KHByZWRpY3Rpb25zX3JmMywgdGVzdF9kYXRhMyRFVU5JU2FfMSlcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuY29uZnVzaW9uTWF0cml4KHByZWRpY3Rpb25zX3JmMywgdGVzdF9kYXRhMyRFVU5JU2FfMSlcbmBgYCJ9 -->
```r
confusionMatrix(predictions_rf3, test_data3$EUNISa_1)
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoiQ29uZnVzaW9uIE1hdHJpeCBhbmQgU3RhdGlzdGljc1xuXG4gICAgICAgICAgUmVmZXJlbmNlXG5QcmVkaWN0aW9uICAgIFEgICAgUiAgICBTICAgIFRcbiAgICAgICAgIFEgIDY3MCAgMjk0ICAxNjggICAgOFxuICAgICAgICAgUiAgMzExIDEzOTAgIDExMyAgIDYwXG4gICAgICAgICBTICAgOTIgIDE1MSAgMzc4ICAgIDBcbiAgICAgICAgIFQgICAgNyAgIDMyICAgIDEgICAzNFxuXG5PdmVyYWxsIFN0YXRpc3RpY3NcbiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIFxuICAgICAgICAgICAgICAgQWNjdXJhY3kgOiAwLjY2NjUgICAgICAgICAgXG4gICAgICAgICAgICAgICAgIDk1JSBDSSA6ICgwLjY1MTEsIDAuNjgxNylcbiAgICBObyBJbmZvcm1hdGlvbiBSYXRlIDogMC41MDM0ICAgICAgICAgIFxuICAgIFAtVmFsdWUgW0FjYyA+IE5JUl0gOiA8IDIuMmUtMTYgICAgICAgXG4gICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICBcbiAgICAgICAgICAgICAgICAgIEthcHBhIDogMC40NjcxICAgICAgICAgIFxuICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgXG4gTWNuZW1hcidzIFRlc3QgUC1WYWx1ZSA6IDEuMjU2ZS0wNiAgICAgICBcblxuU3RhdGlzdGljcyBieSBDbGFzczpcblxuICAgICAgICAgICAgICAgICAgICAgQ2xhc3M6IFEgQ2xhc3M6IFIgQ2xhc3M6IFMgQ2xhc3M6IFRcblNlbnNpdGl2aXR5ICAgICAgICAgICAgMC42MjA0ICAgMC43NDQ1ICAgMC41NzI3IDAuMzMzMzMzXG5TcGVjaWZpY2l0eSAgICAgICAgICAgIDAuODIxMiAgIDAuNzM3MiAgIDAuOTIwMyAwLjk4ODkxMFxuUG9zIFByZWQgVmFsdWUgICAgICAgICAwLjU4NzcgICAwLjc0MTcgICAwLjYwODcgMC40NTk0NTlcbk5lZyBQcmVkIFZhbHVlICAgICAgICAgMC44NDA0ICAgMC43NDAxICAgMC45MDg3IDAuOTgxMjkzXG5QcmV2YWxlbmNlICAgICAgICAgICAgIDAuMjkxMiAgIDAuNTAzNCAgIDAuMTc3OSAwLjAyNzUwMVxuRGV0ZWN0aW9uIFJhdGUgICAgICAgICAwLjE4MDYgICAwLjM3NDggICAwLjEwMTkgMC4wMDkxNjdcbkRldGVjdGlvbiBQcmV2YWxlbmNlICAgMC4zMDc0ICAgMC41MDUzICAgMC4xNjc0IDAuMDE5OTUxXG5CYWxhbmNlZCBBY2N1cmFjeSAgICAgIDAuNzIwOCAgIDAuNzQwOSAgIDAuNzQ2NSAwLjY2MTEyMlxuIn0= -->
```
Confusion Matrix and Statistics
Reference
Prediction Q R S T
Q 670 294 168 8
R 311 1390 113 60
S 92 151 378 0
T 7 32 1 34
Overall Statistics
Accuracy : 0.6665
95% CI : (0.6511, 0.6817)
No Information Rate : 0.5034
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.4671
Mcnemar's Test P-Value : 1.256e-06
Statistics by Class:
Class: Q Class: R Class: S Class: T
Sensitivity 0.6204 0.7445 0.5727 0.333333
Specificity 0.8212 0.7372 0.9203 0.988910
Pos Pred Value 0.5877 0.7417 0.6087 0.459459
Neg Pred Value 0.8404 0.7401 0.9087 0.981293
Prevalence 0.2912 0.5034 0.1779 0.027501
Detection Rate 0.1806 0.3748 0.1019 0.009167
Detection Prevalence 0.3074 0.5053 0.1674 0.019951
Balanced Accuracy 0.7208 0.7409 0.7465 0.661122
```
<!-- rnb-output-end -->
<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1zb3VyY2UtYmVnaW4gZXlKa1lYUmhJam9pWUdCZ2NseHVZMjl1Wm5WemFXOXVUV0YwY21sNEtIQnlaV1JwWTNScGIyNXpYM0ptTkN3Z2RHVnpkRjlrWVhSaE5DUkZWVTVKVTJGZk1TbGNibUJnWUNKOSAtLT5cblxuYGBgclxuY29uZnVzaW9uTWF0cml4KHByZWRpY3Rpb25zX3JmNCwgdGVzdF9kYXRhNCRFVU5JU2FfMSlcbmBgYFxuXG48IS0tIHJuYi1zb3VyY2UtZW5kIC0tPlxuIn0= -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuY29uZnVzaW9uTWF0cml4KHByZWRpY3Rpb25zX3JmNCwgdGVzdF9kYXRhNCRFVU5JU2FfMSlcbmBgYCJ9 -->
```r
confusionMatrix(predictions_rf4, test_data4$EUNISa_1)
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
```
Confusion Matrix and Statistics
Reference
Prediction Q R S T
Q 389 207 136 4
R 228 748 67 33
S 69 114 204 10
T 2 31 12 7
Overall Statistics
Accuracy : 0.5962
95% CI : (0.5756, 0.6165)
No Information Rate : 0.4865
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.3654
Mcnemar's Test P-Value : 2.724e-06
Statistics by Class:
Class: Q Class: R Class: S Class: T
Sensitivity 0.5654 0.6800 0.48687 0.129630
Specificity 0.7794 0.7175 0.89522 0.979610
Pos Pred Value 0.5285 0.6952 0.51385 0.134615
Neg Pred Value 0.8039 0.7030 0.88466 0.978723
Prevalence 0.3043 0.4865 0.18532 0.023883
Detection Rate 0.1720 0.3308 0.09023 0.003096
Detection Prevalence 0.3255 0.4759 0.17559 0.022999
Balanced Accuracy 0.6724 0.6987 0.69105 0.554620
```
<!-- rnb-output-end -->
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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuY29uZnVzaW9uTWF0cml4KHByZWRpY3Rpb25zX3JmNSwgdGVzdF9kYXRhNSRFVU5JU2FfMSlcbmBgYCJ9 -->
```r
confusionMatrix(predictions_rf5, test_data5$EUNISa_1)
```
<!-- rnb-source-end -->
<!-- rnb-output-end -->
<!-- rnb-output-begin 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 -->
```
Confusion Matrix and Statistics
Reference
Prediction Q R S T
Q 340 122 107 2
R 148 711 67 26
S 63 86 198 21
T 12 24 1 1
Overall Statistics
Accuracy : 0.648
95% CI : (0.6262, 0.6693)
No Information Rate : 0.4889
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.4456
Mcnemar's Test P-Value : 2.15e-07
Statistics by Class:
Class: Q Class: R Class: S Class: T
Sensitivity 0.6039 0.7540 0.5308 0.0200000
Specificity 0.8309 0.7556 0.8907 0.9803087
Pos Pred Value 0.5954 0.7468 0.5380 0.0263158
Neg Pred Value 0.8358 0.7625 0.8879 0.9740878
Prevalence 0.2919 0.4889 0.1934 0.0259202
Detection Rate 0.1763 0.3686 0.1026 0.0005184
Detection Prevalence 0.2960 0.4935 0.1908 0.0196993
Balanced Accuracy 0.7174 0.7548 0.7108 0.5001543
```
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
### Variable importance
#### Unconditional
<!-- rnb-text-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
<!-- rnb-text-begin -->
<!-- rnb-text-end -->
<!-- rnb-text-begin -->
##### Plots
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
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plot(varimp_rf21$varimp, margin = c(10, 6, 2, 2))
plot(varimp_rf23$varimp, margin = c(10, 6, 2, 2))````
```r
plot(varimp_rf21$varimp, margin = c(10, 6, 2, 2))
plot(varimp_rf23$varimp, margin = c(10, 6, 2, 2))
<!-- rnb-source-end -->
<!-- rnb-output-end -->
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<!-- rnb-text-begin -->
# INSERT ABOVE: Fit RF models (level 1) with CH
# Session info
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
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<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuc2Vzc2lvbkluZm8oKVxuYGBgIn0= -->
```r
sessionInfo()---
title: "Script to validate points in ReSurvey database using RS data (Satellite Embeddings Dataset)"
subtitle: "Validation done with ALL points (all observations)"
author: "Alicia Valdés"
date: "`r format(Sys.time(), '%d %B %Y')`"
output:
  pdf_document: default
  html_notebook: default
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(warning = FALSE)
```

This R script is used to validate the points in the ReSurvey database using only bands from the Google Satellite Embedding dataset: https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_SATELLITE_EMBEDDING_V1_ANNUAL#description

# Load libraries

```{r}
library(tidyverse)
library(here)
library(gridExtra)
library(readxl)
library(scales)
library(sf)
library(rnaturalearth)
library(dtplyr)
library(lme4)
library(lmerTest)
library(car)
library(ggeffects)
library(party)
library(partykit)
library(moreparty)
library(doParallel)
library(strucchange)
library(ggparty)
library(caret)
library(moreparty)
library(randomForest)
library(pROC)
library(corrplot)
library(rlang)
library(stringr)
library(beepr)
library(foreach)
library(permimp)
library(yardstick)
```

# Define printall function

```{r}
printall <- function(tibble) {
  print(tibble, width = Inf)
  }
```

# Load geom_flat_violin plot

```{r}
source("https://gist.githubusercontent.com/benmarwick/2a1bb0133ff568cbe28d/raw/fb53bd97121f7f9ce947837ef1a4c65a73bffb3f/geom_flat_violin.R")
```

# Load previously created objects

```{r}
# Define the folder path
folder_path <- here("objects", "RF", "Satellite_Embeddings")

# List all .RData or .rda files in the folder
rdata_files <- list.files(folder_path, full.names = TRUE)

# Load each file
lapply(rdata_files, load, envir = .GlobalEnv)
```

# Read data

```{r}
data_validation <- read_tsv(here(
  "data", "clean","final_RS_data_SatEmb_20250922.csv"))
```

No parsing issues!

# Some data managenemt

## TO-DO: Missing data checks

Do when all RS data is ready!

# Distributions all bioregions

```{r}
# Define a function to create histograms
plot_histogram <- function(data, x_var, x_label) {
  ggplot(data %>%
           dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")),
         aes(x = !!sym(x_var))) +
    geom_histogram(color = "black", fill = "white") +
    labs(x = x_label, y = "Frequency") +
    theme_bw()
}
```

```{r}
# Define a function to create plots with violin + boxplot + points
distr_plot <- function(data, y_vars, y_labels) {
  for (i in seq_along(y_vars)) {
    y_var <- y_vars[[i]]
    y_label <- y_labels[[i]]
    
    p <- ggplot(data = data %>%
                  dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")),
                aes(x = EUNISa_1_descr, y = !!sym(y_var), fill = EUNISa_1_descr)) +
      geom_flat_violin(position = position_nudge(x = 0.2, y = 0), alpha = 0.8) +
      geom_point(aes(y = !!sym(y_var), color = EUNISa_1_descr),
                 position = position_jitter(width = 0.15), size = 1, alpha = 0.25) +
      geom_boxplot(width = 0.2, outlier.shape = NA, alpha = 0.5) +
      stat_summary(fun.y = mean, geom = "point", shape = 20, size = 1) +
      stat_summary(fun.data = function(x) data.frame(y = max(x) + 0.1,
                                                     label = length(x)),
                   geom = "text", aes(label = ..label..), vjust = 0.5) +
      labs(y = y_label, x = "EUNIS level 1") +
      scale_x_discrete(labels = function(x) str_wrap(x, width = 15)) +
      guides(fill = FALSE, color = FALSE) +
      theme_bw() + coord_flip()
    
    print(p)
  }
}
```


Histograms to check that max and min values are ok:

```{r}
plot_histogram(data_validation, "A00", "A00")
plot_histogram(data_validation, "A01", "A01")
plot_histogram(data_validation, "A23", "A23")
plot_histogram(data_validation, "A32", "A32")
plot_histogram(data_validation, "A50", "A50")
plot_histogram(data_validation, "A61", "A61")
plot_histogram(data_validation, "A63", "A63")
```

Distribution plots:

```{r message=FALSE, warning=FALSE}
distr_plot(data_validation,
           c("A00", "A01", "A10", "A22", "A33",
             "A40", "A53", "A61", "A62", "A63"),
           c("A00", "A01", "A10", "A22", "A33",
             "A40", "A53", "A61", "A62", "A63"))
```

# Distributions per bioregion

```{r}
# Define a function to create plots with violin + boxplot + points
distr_plot_biogeo <- function(data, y_vars, y_labels) {
  plots <- list()
  
  for (i in seq_along(y_vars)) {
    y_var <- y_vars[[i]]
    y_label <- y_labels[[i]]
    
    p <- ggplot(data = data %>%
                  dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")),
                aes(x = EUNISa_1_descr, y = !!sym(y_var), fill = EUNISa_1_descr)) +
      geom_flat_violin(position = position_nudge(x = 0.2, y = 0), alpha = 0.8) +
      geom_point(aes(y = !!sym(y_var), color = EUNISa_1_descr),
                 position = position_jitter(width = 0.15), size = 1, alpha = 0.25) +
      geom_boxplot(width = 0.2, outlier.shape = NA, alpha = 0.5) +
      stat_summary(fun.y = mean, geom = "point", shape = 20, size = 1) +
      stat_summary(fun.data = function(x) data.frame(y = max(x) + 0.1,
                                                     label = length(x)),
                   geom = "text", aes(label = ..label..), vjust = 0.5) +
      labs(y = y_label, x = "EUNISa_1_descr") +
      scale_x_discrete(labels = function(x) str_wrap(x, width = 15)) +
      guides(fill = FALSE, color = FALSE) +
      theme_bw() + coord_flip() + facet_wrap(~ biogeo)
    
    plots[[y_var]] <- p
  }
  
  return(plots)
}
```

Distribution plots:

```{r message=FALSE, warning=FALSE}
distr_plot_biogeo(data_validation,
           c("A00", "A01", "A10", "A22", "A33",
             "A40", "A53", "A61", "A62", "A63"),
           c("A00", "A01", "A10", "A22", "A33",
             "A40", "A53", "A61", "A62", "A63"))
```

# Define functions for RF models

## Function for fitting RF models

RF models fitted using the conditional inference version of random forest (first cforest in package party, now fastcforest in package moreparty). Suggested if the data are highly correlated. Cforest is more stable in deriving variable importance values in the presence of highly correlated variables, thus providing better accuracy in calculating variable importance (ref below).

Hothorn, T., Hornik, K. and Zeileis, A. (2006) Unbiased Recursive Portioning: A Conditional Inference Framework. Journal of Computational and Graphical Statistics, 15, 651-
674. http://dx.doi.org/10.1198/106186006X133933

Choose the hyperparameter mtry based on the square root of the number of predictor variables:

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical
learning: Data mining, inference, and prediction. Springer Science &
Business Media.

Maybe TO_DO:
We variated ntree from 50 to 800 in steps of 50, leaving mtry constant at 2. Tis parameter variation showed that ntree=500 was optimal, while higher ntree led to no further model improvement (Supplementary Fig. S10). Subsequently, the hyperparameter mtry was varied from 2 to 8 with constant ntree=500. Here, mtry=3 led to the best results in almost all cases (Supplementary Fig. S11). Consequently, we chose ntree=500 and mtry=3 for our main analysis across all study sites.

Define a function to run fastcforest models:

```{r}
run_rf <- function(vars_RF, train_data, response_var, ntree = 500) 
  {
  
  # Detect and register available cores (leave one free)
  n_cores <- parallel::detectCores() - 1
  cl <- makeCluster(n_cores)
  registerDoParallel(cl)
  
  train_name <- deparse(substitute(train_data))
  
  # Export necessary variables to the cluster
  clusterExport(cl, varlist = c("vars_RF", "train_data", "response_var"),
                envir = environment())

  
  # Set seed for reproducibility
  set.seed(123)
  
  # Measure execution time
  execution_time <- system.time({
    rf_model <- fastcforest(
      formula = reformulate(vars_RF, response = response_var),
      data = train_data,
      controls = party::cforest_control(
        mtry = round(sqrt(length(vars_RF))),
        ntree = ntree
      ),
      parallel = TRUE
    )
  })
  
  # Stop the cluster
  stopCluster(cl)
  
  # Return both the model and execution time
  list(model = rf_model, time = execution_time)
}
```

## Function to compute variable importance

```{r}
# compute_varimp <- function(model, nperm = 100, 
#                                    n_cores = parallel::detectCores() - 1) {
#   # Set up parallel backend
#   cl <- makeCluster(n_cores)
#   registerDoParallel(cl)
#   
#   # Measure execution time
#   execution_time <- system.time({
#     varimp_list <- foreach(i = 1:nperm, .combine = '+', 
#                            .packages = "party") %dopar% {
#       varimp(model, conditional = FALSE, nperm = 1)
#     }
#   })
#   
#   stopCluster(cl)
#   
#   # Average the results
#   varimp_avg <- varimp_list / nperm
#   
#   return(list(varimp = varimp_avg, time = execution_time))
# }
```

Using permimp() en permimp package:
https://cran.r-project.org/web/packages/permimp/vignettes/permimp-package.html#fn1

```{r}
compute_varimp <- function(model, nperm = 100) {

  # Measure execution time
  execution_time <- system.time({
    varimp_result <- permimp(model, conditional = FALSE, progressBar = TRUE)
  })

  return(list(varimp = varimp_result, time = execution_time))
}
```

## Function to compute CONDITIONAL variable importance

```{r}
compute_varimp_cond <- function(model, nperm = 100) {

  # Measure execution time
  execution_time <- system.time({
    varimp_result <- permimp(model, conditional = TRUE, progressBar = TRUE)
  })

  return(list(varimp = varimp_result, time = execution_time))
}
```

## Function to compute ROC (level 1)

```{r}
compute_roc_level1 <- function(model, test_data) {
  # Measure execution time
  execution_time <- system.time({
    # Step 1: Predict probabilities
    probabilities <- predict(model, newdata = test_data, type = "prob")
    
    # Step 2: Convert list of matrices to a proper data frame
    prob_matrix <- t(sapply(probabilities, as.vector))
    prob_df <- as.data.frame(prob_matrix)
    colnames(prob_df) <- c("Q", "R", "S", "T")
    
    # Step 3: Prepare actual class labels
    actual <- factor(test_data$EUNISa_1, levels = c("Q", "R", "S", "T"))
    
    # Step 4: Binarize actual labels
    actual_bin <- model.matrix(~ actual - 1)
    colnames(actual_bin) <- gsub("actual", "", colnames(actual_bin))
    
    # Step 5: Compute ROC data for each class
    roc_data <- lapply(levels(actual), function(class) {
      roc_obj <- roc(actual_bin[, class], prob_df[[class]])
      auc_val <- round(auc(roc_obj), 3)
      data.frame(
        FPR = rev(roc_obj$specificities),
        TPR = rev(roc_obj$sensitivities),
        Class = paste0(class, " (AUC = ", auc_val, ")")
      )
    }) %>% bind_rows()
  })
  
  # Return both ROC data and execution time
  return(list(roc = roc_data, time = execution_time))
}
```

## Function to compute ROC (level 2)

```{r}
compute_roc_level2 <- function(model, test_data) {
  # Measure execution time
  execution_time <- system.time({
    # Step 1: Predict probabilities
    probabilities <- predict(model, newdata = test_data, type = "prob")
    
    # Step 2: Convert list of matrices to a proper data frame
    prob_matrix <- t(sapply(probabilities, as.vector))
    prob_df <- as.data.frame(prob_matrix)
    colnames(prob_df) <- c("Q1", "Q2", "Q4", "Q5", "R1", "R2", "R3", "R4", "R5",
                           "R6", "S3", "S4", "T1", "T3")
    
    # Step 3: Prepare actual class labels
    actual <- factor(test_data$EUNISa_2, 
                     levels = c("Q1", "Q2", "Q4", "Q5", "R1", "R2", "R3", "R4",
                                "R5", "R6", "S3", "S4", "T1", "T3"))
    
    # Step 4: Binarize actual labels
    actual_bin <- model.matrix(~ actual - 1)
    colnames(actual_bin) <- gsub("actual", "", colnames(actual_bin))
    
    # Step 5: Compute ROC data for each class
    roc_data <- lapply(levels(actual), function(class) {
      roc_obj <- roc(actual_bin[, class], prob_df[[class]])
      auc_val <- round(auc(roc_obj), 3)
      data.frame(
        FPR = rev(roc_obj$specificities),
        TPR = rev(roc_obj$sensitivities),
        Class = paste0(class, " (AUC = ", auc_val, ")")
      )
    }) %>% bind_rows()
  })
  
  # Return both ROC data and execution time
  return(list(roc = roc_data, time = execution_time))
}
```

# Define list of predictor vars

```{r}
vars_RF_SatEmb <- colnames(
  data_validation)[startsWith(colnames(data_validation), "A")]
vars_RF_SatEmb_CH <- c(vars_RF_SatEmb, "canopy_height")
```

# Correlation

Correlation of all variables to be included in RF models:

```{r}
corrplot(data_validation %>% 
           dplyr::select(starts_with("A")) %>%
           cor(use = "pairwise.complete.obs"),
         method = "color", type = "upper", tl.col = "black", tl.srt = 45)
corrplot(data_validation %>% 
           dplyr::select(starts_with("A"), canopy_height) %>%
           cor(use = "pairwise.complete.obs"),
         method = "color", type = "upper", tl.col = "black", tl.srt = 45)
```

# Rough validation

Define a set of rules for a first validation of ALL ReSurvey data ("Expert-based" rules). Not very ambitious, only taking out observations that are clearly wrong on the basis of indicator values.

Create column for first validation based on different indicators, where "wrong" is noted when the validation rule is not met. 

Here only using validation based on canopy height, not NDWI.

Define rules:

```{r}
data_validation <-
  data_validation %>%
  mutate(
    valid_1_CH = case_when(
      # R & Q points with high CH
      EUNISa_1 %in% c("R", "Q") & canopy_height > 2 ~ "wrong",
      # T points with low CH
      EUNISa_1 == "T" & canopy_height < 3 ~ "wrong",
      # S points with high CH
      EUNISa_1 == "S" & canopy_height > 3 ~ "wrong",
      TRUE ~ NA_character_),
    # Count how many validation rules are not met
    valid_1_count = rowSums(across(c(valid_1_CH), ~ . == "wrong"),
                            na.rm = TRUE),
    # Points where at least 1 rule not met
    valid_1 = if_else(valid_1_count > 0, "At least 1 rule broken",
                      "No rules broken so far")
    )
```

# Fit RF models (level 1)

## Without refinement

### Get filtered data

```{r}
# No validation
data_validation_novalid <- data_validation %>%
  # Select only GPS points
  dplyr::filter(Lctnmth == "Location with GPS" |
                  Lctnmth == "Location with differential GPS") %>%
  select(-Lctnmth) %>%
  mutate(EUNISa_1 = as.factor(EUNISa_1))

# Rough validation
data_validation_roughvalid <- data_validation %>%
  # Select only GPS points
  dplyr::filter(Lctnmth == "Location with GPS" |
                  Lctnmth == "Location with differential GPS") %>%
  # Filter out points that have not passed the rough validation
  dplyr::filter(valid_1 == "No rules broken so far") %>%
  select(-Lctnmth, -valid_1) %>%
  mutate(EUNISa_1 = as.factor(EUNISa_1))
```

### N points per category

```{r}
bind_rows(
  data_validation_novalid %>% count(EUNISa_1) %>%
    pivot_wider(names_from = EUNISa_1, values_from = n) %>% 
    mutate(data = "data_validation_novalid") %>%
    select(data, Q, R, S, T),
  data_validation_roughvalid %>% count(EUNISa_1) %>%
    pivot_wider(names_from = EUNISa_1, values_from = n) %>% 
    mutate(data = "data_validation_roughvalid") %>%
    select(data, Q, R, S, T),
  )
```

### Split into training and test data sets

```{r}
set.seed(123)
```

```{r}
train_indices_novalid <- sample(1:nrow(data_validation_novalid), 
                         0.7 * nrow(data_validation_novalid))
train_indices_roughvalid <- sample(1:nrow(data_validation_roughvalid), 
                         0.7 * nrow(data_validation_roughvalid))
```

```{r}
train_data_novalid <- data_validation_novalid[train_indices_novalid, ]
train_data_roughvalid <- data_validation_roughvalid[train_indices_roughvalid, ]
```

```{r}
test_data_novalid <- data_validation_novalid[-train_indices_novalid, ]
test_data_roughvalid <- data_validation_roughvalid[-train_indices_roughvalid, ]
```

### Fit models

```{r eval=FALSE, include=FALSE}
# 1: No validation, SatEmb bands
rf1 <- run_rf(vars_RF_SatEmb, train_data_novalid, "EUNISa_1")
# 2: No validation, SatEmb bands + CH
rf2 <- run_rf(vars_RF_SatEmb_CH, train_data_novalid, "EUNISa_1")
# 3: Rough validation, SatEmb bands
rf3 <- run_rf(vars_RF_SatEmb, train_data_roughvalid, "EUNISa_1")
# 4: Rough validation, SatEmb bands + CH
rf4 <- run_rf(vars_RF_SatEmb_CH, train_data_roughvalid, "EUNISa_1")
```

```{r}
print(rf1$time)
print(rf2$time)
print(rf3$time)
print(rf4$time)
```

```{r eval=FALSE, include=FALSE}
save(rf1, file = "objects/RF/Satellite_Embeddings/rf1_model_l1_novalid_norefin_SatEmb.Rdata")
save(rf2, file = "objects/RF/Satellite_Embeddings/rf2_model_l1_novalid_norefin_Sat_Emb_CH.Rdata")
save(rf3, file = "objects/RF/Satellite_Embeddings/rf3_model_l1_roughvalid_norefin_Sat_Emb.Rdata")
save(rf4, file = "objects/RF/Satellite_Embeddings/rf4_model_l1_roughvalid_norefin_sat_Emb_CH.Rdata")
```

### Predictions

```{r eval=FALSE, include=FALSE}
predictions_rf1 <- predict(rf1$model, newdata = test_data_novalid,OOB = TRUE,
                           type = "response")
predictions_rf2 <- predict(rf2$model, newdata = test_data_novalid,OOB = TRUE,
                           type = "response")
predictions_rf3 <- predict(rf3$model, newdata = test_data_roughvalid,OOB = TRUE,
                           type = "response")
predictions_rf4 <- predict(rf4$model, newdata = test_data_roughvalid,OOB = TRUE,
                           type = "response")
```

```{r eval=FALSE, include=FALSE}
save(predictions_rf1, file = "objects/RF/Satellite_Embeddings/rf1_pred_l1_novalid_norefin_SatEmb.Rdata")
save(predictions_rf2, file = "objects/RF/Satellite_Embeddings/rf1_pred_l1_novalid_norefin_SatEmb_CH.Rdata")
save(predictions_rf3, file = "objects/RF/Satellite_Embeddings/rf3_pred_l1_roughvalid_norefin_Sat_Emb.Rdata")
save(predictions_rf4, file = "objects/RF/Satellite_Embeddings/rf3_pred_l1_roughvalid_norefin_Sat_Emb_CH.Rdata")
```

### Confusion matrices

```{r}
# 1: No validation, SatEmb bands
confusionMatrix(predictions_rf1, test_data_novalid$EUNISa_1)
# 2: No validation, SatEmb bands + CH
confusionMatrix(predictions_rf2, test_data_novalid$EUNISa_1)
# 3: Rough validation, SatEmb bands
confusionMatrix(predictions_rf3, test_data_roughvalid$EUNISa_1)
# 4: Rough validation, SatEmb bands + CH
confusionMatrix(predictions_rf4, test_data_roughvalid$EUNISa_1)
```

### Variable importance

#### Unconditional

```{r eval=FALSE, include=FALSE}
varimp_rf1 <- compute_varimp(rf1$model, nperm = 100)
varimp_rf2 <- compute_varimp(rf2$model, nperm = 100)
varimp_rf3 <- compute_varimp(rf3$model, nperm = 100)
varimp_rf4 <- compute_varimp(rf4$model, nperm = 100)
```

```{r eval=FALSE, include=FALSE}
print(varimp_rf1$time)
print(varimp_rf2$time)
print(varimp_rf3$time)
print(varimp_rf4$time)
```

```{r eval=FALSE, include=FALSE}
save(varimp_rf1, file = "objects/RF/Satellite_Embeddings/rf1_varimp_l1_novalid_norefin_SatEmb.Rdata")
save(varimp_rf2, file = "objects/RF/Satellite_Embeddings/rf2_varimp_l1_novalid_norefin_SatEmb_CH.Rdata")
save(varimp_rf3, file = "objects/RF/Satellite_Embeddings/rf3_varimp_l1_roughvalid_norefin_SatEmb.Rdata")
save(varimp_rf4, file = "objects/RF/Satellite_Embeddings/rf4_varimp_l1_roughvalid_norefin_SatEmb_CH.Rdata")
```

##### Plots

```{r}
plot(varimp_rf1$varimp, margin = c(10, 6, 2, 2))
plot(varimp_rf2$varimp, margin = c(10, 6, 2, 2))
plot(varimp_rf3$varimp, margin = c(10, 6, 2, 2))
plot(varimp_rf4$varimp, margin = c(10, 6, 2, 2))
```




# HERE



## No validation

### No refinement

#### SatEmb bands

#### SatEmb + CH

### Refinement 10-90th: 1 variable

### Refinement 10-90th: 2 variables

### Refinement 10-90th: 3 variables

### Refinement 10-90th: 4 variables


## Rough validation


## Without refinement

### Get filtered data

```{r}
data_validation_GPS <- data_validation %>%
  # Select only GPS points
  dplyr::filter(Lctnmth == "Location with GPS" |
                  Lctnmth == "Location with differential GPS") %>%
  select(-Lctnmth) %>%
  mutate(EUNISa_1 = as.factor(EUNISa_1))
```

#### N points per category

```{r}
data_validation_GPS %>% count(EUNISa_1)
```

### Split into training and test data sets

```{r}
set.seed(123)
```

```{r}
train_indices1 <- sample(1:nrow(data_validation_GPS), 
                         0.7 * nrow(data_validation_GPS))
```

```{r}
train_data1 <- data_validation_GPS[train_indices1, ]
```

```{r}
test_data1 <- data_validation_GPS[-train_indices1, ]
```

### Fit models

```{r eval=FALSE, include=FALSE}
rf1 <- run_rf(vars_RF_SatEmb, train_data1, "EUNISa_1")
rf1_CH <- run_rf(vars_RF_SatEmb_CH, train_data1, "EUNISa_1")
```

```{r}
print(rf1$time)
print(rf1_CH$time)
```

```{r eval=FALSE, include=FALSE}
save(rf1, file = "objects/RF/Satellite_Embeddings/rf1_model_l1_novalid_GPS_SatEmb.Rdata")
save(rf1_CH, file = "objects/RF/Satellite_Embeddings/rf1_model_l1_novalid_GPS_SatEmb_CH.Rdata")
```

### Predictions

```{r eval=FALSE, include=FALSE}
predictions_rf1 <- predict(rf1$model, newdata = test_data1,OOB = TRUE,
                           type = "response")
predictions_rf1_CH <- predict(rf1_CH$model, newdata = test_data1,OOB = TRUE,
                           type = "response")
```

```{r eval=FALSE, include=FALSE}
save(predictions_rf1, file = "objects/RF/Satellite_Embeddings/rf1_pred_l1_novalid_GPS_SatEmb.Rdata")
save(predictions_rf1_CH, file = "objects/RF/Satellite_Embeddings/rf1_pred_l1_novalid_GPS_SatEmb_CH.Rdata")
```

### Confusion matrix

```{r}
confusionMatrix(predictions_rf1, test_data1$EUNISa_1)
confusionMatrix(predictions_rf1_CH, test_data1$EUNISa_1)
```
### Variable importance

#### Unconditional

```{r eval=FALSE, include=FALSE}
varimp_rf1 <- compute_varimp(rf1$model, nperm = 100)
```

```{r eval=FALSE, include=FALSE}
print(varimp_rf1$time)
```

```{r eval=FALSE, include=FALSE}
save(varimp_rf1, file = "objects/RF/S2/rf1_varimp_l1_slope_novalid_GPS_S2.Rdata")
```

##### Plot

```{r}
plot(varimp_rf1$varimp, margin = c(10, 6, 2, 2))
```

#### Conditional (TBD)

```{r eval=FALSE, include=FALSE}
varimp-cond_rf1 <- compute_varimp_cond(rf1$model, nperm = 100)
```

```{r eval=FALSE, include=FALSE}
print(varimp-cond_rf1$time)
```

```{r eval=FALSE, include=FALSE}
save(varimp-cond_rf1, 
     file = "objects/RF/S2/rf1_varimp-cond_l1_slope_novalid_GPS_S2.Rdata")
```

##### Plot

```{r}
plot(varimp-cond_rf1$varimp, margin = c(9, 3, 2, 2))
```

### ROC curves (TBD)

```{r eval=FALSE, include=FALSE}
roc_data1 <- compute_roc_level1(rf1$model, test_data1)
```

```{r}
print(roc_data1$time)
```

```{r}
save(roc_data1, 
     file = "objects/RF/S2/rf1_rocdata_l1_slope_novalid_GPS_S2.Rdata")
```

#### Plot

```{r}
roc1 <- ggplot(roc_data1$roc, aes(x = FPR, y = TPR, color = Class)) +
  geom_line(size = 1.2) + geom_abline(linetype = "dashed", color = "gray") +
  labs(title = "Multiclass ROC Curves with AUC", x = "False Positive Rate",
       y = "True Positive Rate", color = "Class (AUC)") +
  theme_minimal() + theme(legend.position = "bottom")
```

```{r}
roc1
```

## With refinement

### Refinement

Based on the 1st, 2nd, 3rd, 4th... most important variables: A59, A15, A12, A60.

```{r}
distr_plot_percentiles <- function(data, y_vars, y_labels) {
  for (i in seq_along(y_vars)) {
    y_var <- y_vars[[i]]
    y_label <- y_labels[[i]]
    
    # Calculate percentiles per EUNISa_1 group
    percentiles <- data %>%
      group_by(EUNISa_1) %>%
      summarise(
        p10 = quantile(.data[[y_var]], 0.1, na.rm = TRUE),
        p90 = quantile(.data[[y_var]], 0.9, na.rm = TRUE),
        .groups = "drop"
      )
    
    # Join percentiles back to data
    data_flagged <- data %>%
      left_join(percentiles, by = "EUNISa_1") %>%
      mutate(outlier_flag = case_when(
        .data[[y_var]] < p10 ~ "low",
        .data[[y_var]] > p90 ~ "high",
        TRUE ~ "mid"
      ))
    
    # Filter and plot
    p <- ggplot(data = data_flagged %>%
                  dplyr::filter(EUNISa_1 %in% c("T", "R", "S", "Q")),
                aes(x = EUNISa_1_descr, y = .data[[y_var]])) +
      geom_flat_violin(aes(fill = EUNISa_1_descr),
                       position = position_nudge(x = 0.2, y = 0), alpha = 0.8) +
      geom_point(aes(color = ifelse(outlier_flag == "mid",
                                    EUNISa_1_descr, "grey")),
                 position = position_jitter(width = 0.15), size = 1,
                 alpha = 0.6) +
      geom_boxplot(aes(fill = EUNISa_1_descr), width = 0.2, outlier.shape = NA,
                   alpha = 0.5) +
      stat_summary(fun = mean, geom = "point", shape = 20, size = 1) +
      stat_summary(fun.data = function(x) data.frame(y = max(x, na.rm = TRUE) +
                                                       0.1, label = length(x)),
                   geom = "text", aes(label = ..label..), vjust = 0.5) +
      labs(y = y_label, x = "EUNIS level 1") +
      scale_x_discrete(labels = function(x) str_wrap(x, width = 15)) +
      guides(fill = FALSE, color = FALSE) +
      theme_bw() + coord_flip() +
      scale_color_manual(values = c(
        "Forests and other wooded land" = "#F8766D",
        "Grasslands" = "#7CAE00",
        "Heathlands, scrub and tundra" = "#00BFC4",
        "Wetlands" = "#C77CFF",
        "grey" = "grey"))
    
    print(p)
  }
}
```

```{r}
distr_plot_percentiles(
  # GPS points
  data_validation_GPS,
  c("A59", "A15", "A12", "A60"),
  c("A59", "A15", "A12", "A60"))
```

Calculate percentiles:

```{r}
percentiles <- 
  data_validation_GPS %>%
  group_by(EUNISa_1) %>%
  summarize(
    perc_10_A59 = quantile(A59, probs = 0.10, na.rm = T),
    perc_20_A59 = quantile(A59, probs = 0.20, na.rm = T),
    perc_80_A59 = quantile(A59, probs = 0.80, na.rm = T),
    perc_90_A59 = quantile(A59, probs = 0.90, na.rm = T),
    perc_10_A15 = quantile(A15, probs = 0.10, na.rm = T),
    perc_20_A15 = quantile(A15, probs = 0.20, na.rm = T),
    perc_80_A15 = quantile(A15, probs = 0.80, na.rm = T),
    perc_90_A15 = quantile(A15, probs = 0.90, na.rm = T),
    perc_10_A12 = quantile(A12, probs = 0.10, na.rm = T),
    perc_20_A12 = quantile(A12, probs = 0.20, na.rm = T),
    perc_80_A12 = quantile(A12, probs = 0.80, na.rm = T),
    perc_90_A12 = quantile(A12, probs = 0.90, na.rm = T),
    perc_10_A60 = quantile(A60, probs = 0.10, na.rm = T),
    perc_20_A60 = quantile(A60, probs = 0.20, na.rm = T),
    perc_80_A60 = quantile(A60, probs = 0.80, na.rm = T),
    perc_90_A60 = quantile(A60, probs = 0.90, na.rm = T),
    )
```

### Get filtered data

```{r}
data_validation_GPS_refin1 <- 
  data_validation_GPS %>%
  left_join(percentiles, by = "EUNISa_1") %>%
  mutate(EUNISa_1 = as.factor(EUNISa_1)) %>%
  dplyr::filter(
    (A59 >= perc_10_A59 & A59 <= perc_90_A59)
    )
data_validation_GPS_refin2 <- 
  data_validation_GPS %>%
  left_join(percentiles, by = "EUNISa_1") %>%
  mutate(EUNISa_1 = as.factor(EUNISa_1)) %>%
  dplyr::filter(
    (A59 >= perc_10_A59 & A59 <= perc_90_A59) &
      (A15 >= perc_10_A15 & A15 <= perc_90_A15)
    )
data_validation_GPS_refin3 <- 
  data_validation_GPS %>%
  left_join(percentiles, by = "EUNISa_1") %>%
  mutate(EUNISa_1 = as.factor(EUNISa_1)) %>%
  dplyr::filter(
    (A59 >= perc_10_A59 & A59 <= perc_90_A59) &
      (A15 >= perc_10_A15 & A15 <= perc_90_A15) &
      (A12 >= perc_10_A12 & A12 <= perc_90_A12)
    )
data_validation_GPS_refin4 <- 
  data_validation_GPS %>%
  left_join(percentiles, by = "EUNISa_1") %>%
  mutate(EUNISa_1 = as.factor(EUNISa_1)) %>%
  dplyr::filter(
    (A59 >= perc_10_A59 & A59 <= perc_90_A59) &
      (A15 >= perc_10_A15 & A15 <= perc_90_A15) &
      (A12 >= perc_10_A12 & A12 <= perc_90_A12) &
      (A60 >= perc_10_A60 & A60 <= perc_90_A60)
    )
```

#### N plots per category

```{r}
bind_rows(
  data_validation_GPS_refin1 %>% count(EUNISa_1) %>%
    pivot_wider(names_from = EUNISa_1, values_from = n) %>% 
    mutate(data = "data_validation_GPS_refin1") %>%
    select(data, Q, R, S, T),
  data_validation_GPS_refin2 %>% count(EUNISa_1) %>%
    pivot_wider(names_from = EUNISa_1, values_from = n) %>% 
    mutate(data = "data_validation_GPS_refin2") %>%
    select(data, Q, R, S, T),
  data_validation_GPS_refin3 %>% count(EUNISa_1) %>%
    pivot_wider(names_from = EUNISa_1, values_from = n) %>% 
    mutate(data = "data_validation_GPS_refin3") %>%
    select(data, Q, R, S, T),
  data_validation_GPS_refin4 %>% count(EUNISa_1) %>%
    pivot_wider(names_from = EUNISa_1, values_from = n) %>% 
    mutate(data = "data_validation_GPS_refin4") %>%
    select(data, Q, R, S, T)
  )
```

### Split into training and test data sets

```{r}
set.seed(123)
```

```{r}
train_indices2 <- sample(1:nrow(data_validation_GPS_refin1), 
                         0.7 * nrow(data_validation_GPS_refin1))
train_indices3 <- sample(1:nrow(data_validation_GPS_refin2), 
                         0.7 * nrow(data_validation_GPS_refin3))
train_indices4 <- sample(1:nrow(data_validation_GPS_refin3), 
                         0.7 * nrow(data_validation_GPS_refin3))
train_indices5 <- sample(1:nrow(data_validation_GPS_refin4), 
                         0.7 * nrow(data_validation_GPS_refin4))
```

```{r}
train_data2 <- data_validation_GPS_refin1[train_indices2, ]
train_data3 <- data_validation_GPS_refin2[train_indices3, ]
train_data4 <- data_validation_GPS_refin3[train_indices4, ]
train_data5 <- data_validation_GPS_refin4[train_indices5, ]
```

```{r}
test_data2 <- data_validation_GPS_refin1[-train_indices2, ]
test_data3 <- data_validation_GPS_refin2[-train_indices3, ]
test_data4 <- data_validation_GPS_refin3[-train_indices4, ]
test_data5 <- data_validation_GPS_refin4[-train_indices5, ]
```

### Fit models

```{r eval=FALSE, include=FALSE}
rf2 <- run_rf(vars_RF_SatEmb, train_data2, "EUNISa_1")
rf3 <- run_rf(vars_RF_SatEmb, train_data3, "EUNISa_1")
rf4 <- run_rf(vars_RF_SatEmb, train_data4, "EUNISa_1")
rf5 <- run_rf(vars_RF_SatEmb, train_data5, "EUNISa_1")
```

```{r}
print(rf2$time)
print(rf3$time)
print(rf4$time)
print(rf5$time)
```

```{r eval=FALSE, include=FALSE}
save(rf2, 
     file = "objects/RF/Satellite_Embeddings/rf2_model_l1_novalid-refin1090_1v_GPS_SatEmb.Rdata")
save(rf3, 
     file = "objects/RF/Satellite_Embeddings/rf3_model_l1_novalid-refin1090_2v_GPS_SatEmb.Rdata")
save(rf4, 
     file = "objects/RF/Satellite_Embeddings/rf4_model_l1_novalid-refin1090_3v_GPS_SatEmb.Rdata")
save(rf5, 
     file = "objects/RF/Satellite_Embeddings/rf5_model_l1_novalid-refin1090_4v_GPS_SatEmb.Rdata")
```

### Predictions

```{r eval=FALSE, include=FALSE}
predictions_rf2 <- predict(rf2$model, newdata = test_data2,OOB = TRUE,
                           type = "response")
predictions_rf3 <- predict(rf3$model, newdata = test_data3,OOB = TRUE,
                           type = "response")
predictions_rf4 <- predict(rf4$model, newdata = test_data4,OOB = TRUE,
                           type = "response")
predictions_rf5 <- predict(rf5$model, newdata = test_data5,OOB = TRUE,
                           type = "response")
```

```{r eval=FALSE, include=FALSE}
save(predictions_rf2, 
     file = "objects/RF/Satellite_Embeddings/rf2_pred_l1_novalid-refin1090_1v_GPS_SatEmb.Rdata.Rdata")
save(predictions_rf3, 
     file = "objects/RF/Satellite_Embeddings/rf3_pred_l1_novalid-refin1090_2v_GPS_SatEmb.Rdata.Rdata")
save(predictions_rf4, 
     file = "objects/RF/Satellite_Embeddings/rf4_pred_l1_novalid-refin1090_3v_GPS_SatEmb.Rdata.Rdata")
save(predictions_rf5, 
     file = "objects/RF/Satellite_Embeddings/rf5_pred_l1_novalid-refin1090_4v_GPS_SatEmb.Rdata.Rdata")
```

### Confusion matrices

```{r}
confusionMatrix(predictions_rf2, test_data2$EUNISa_1)
confusionMatrix(predictions_rf3, test_data3$EUNISa_1)
confusionMatrix(predictions_rf4, test_data4$EUNISa_1)
confusionMatrix(predictions_rf5, test_data5$EUNISa_1)
```

### Variable importance

#### Unconditional

```{r eval=FALSE, include=FALSE}
varimp_rf2 <- compute_varimp(rf2$model, nperm = 100)
varimp_rf3 <- compute_varimp(rf3$model, nperm = 100)
varimp_rf4 <- compute_varimp(rf4$model, nperm = 100)
varimp_rf5 <- compute_varimp(rf5$model, nperm = 100)
```

```{r eval=FALSE, include=FALSE}
print(varimp_rf2$time)
print(varimp_rf3$time)
print(varimp_rf4$time)
print(varimp_rf5$time)
```

```{r eval=FALSE, include=FALSE}
save(varimp_rf2,
     file = "objects/RF/Satellite_Embeddings/rf2_varimp_l1_novalid-refin1090_1v_GPS_SatEmb.Rdata.Rdata")
save(varimp_rf3,
     file = "objects/RF/Satellite_Embeddings/rf3_varimp_l1_novalid-refin1090_2v_GPS_SatEmb.Rdata.Rdata")
save(varimp_rf4,
     file = "objects/RF/Satellite_Embeddings/rf4_varimp_l1_novalid-refin1090_3v_GPS_SatEmb.Rdata.Rdata")
save(varimp_rf5,
     file = "objects/RF/Satellite_Embeddings/rf5_varimp_l1_novalid-refin1090_4v_GPS_SatEmb.Rdata.Rdata")
```

##### Plots

```{r}
plot(varimp_rf2$varimp, margin = c(10, 6, 2, 2))
plot(varimp_rf3$varimp, margin = c(10, 6, 2, 2))
plot(varimp_rf4$varimp, margin = c(10, 6, 2, 2))
plot(varimp_rf5$varimp, margin = c(10, 6, 2, 2))
```

# INSERT ABOVE: Fit RF models (level 1) with CH



# Session info

```{r}
sessionInfo()
```

